Extracted Text

PLUG ANDPLAYLANGUAGEMODELS:ASIMPLE
APPROACH TOCONTROLLEDTEXTGENERATION
Sumanth Dathathri

CMS, Caltech
Andrea Madotto

HKUST
Janice Lan
Uber AI
Jane Hung
Uber AI
Eric Frank
Uber AI
Piero Molino
Uber AI
Jason Yosinski
yy
Uber AI
Rosanne Liu
y
Uber AI
sdathath@caltech.edu, amadotto@connect.ust.hk
{janlan, jane.hung, mysterefrank, piero, yosinski, rosanne}@uber.com
ABSTRACT
Large transformer-based language models (LMs) trained on huge text corpora
have shown unparalleled generation capabilities. However, controlling attributes
of the generated language (e.g. switching topic or sentiment) is difcult without
modifying the model architecture or ne-tuning on attribute-specic data and en-
tailing the signicant cost of retraining. We propose a simple alternative: the Plug
and Play Language Model (PPLM) for controllable language generation, which
combines a pretrained LM with one or more simple attribute classiers that guide
text generation without any further training of the LM. In the canonical scenario
we present, the attribute models are simple classiers consisting of a user-specied
bag of words or a single learned layer with 100,000 times fewer parameters than
the LM. Sampling entails a forward and backward pass in which gradients from
the attribute model push the LM's hidden activations and thus guide the gener-
ation. Model samples demonstrate control over a range of topics and sentiment
styles, and extensive automated and human annotated evaluations show attribute
alignment and uency. PPLMs are exible in that any combination of differen-
tiable attribute models may be used to steer text generation, which will allow for
diverse and creative applications beyond the examples given in this paper.
1 INTRODUCTION
The Transformer architecture (Vaswani et al., 2017) has enabled large-scale language models (LMs)
trained on a huge amount of data (Radford et al., 2019; Dai et al., 2019b; Radford et al., 2018b) to
greatly improve the state-of-the-art on natural language processing tasks. These models are used to
extract contextualized word embeddings for transfer learning purposes (Devlin et al., 2019) and as
natural language generators. The latter can leverage large amounts of unannotated data and a simple
log-likelihood training objective. However, once such models are trained, controlling attributes of

Work done during internship at Uber AI
y
Co-senior authors .
Summary of contributions: Sumanth, Rosanne, and Jason conceptualized PPLMs and led the manuscript
writing. Sumanth led the project, implemented the PPLM, set up and ran all modeling experiments, engineered
how to obtain workable gradients via the weighted embedding approach, and made the model work. Andrea
helped with preparing datasets for discriminator training, automated evaluation, running experiments, and writ-
ing the manuscript. Sumanth, Rosanne, and Andrea ran the external baselines. Rosanne and Janice built and
oversaw the human evaluation pipeline and computed the statistics. Jane ran the story generation with skeleton
prexes. Eric assisted with detoxication experiments and drew multiple versions of Wooly. Piero led efforts
to migrate to the new pytorch transformer, helped with code release, and Rosanne, Jason, and Piero coordinated
with collaborators from Hugging Face to produce the demo. Jason helped with the annotation pipeline, nding
bugs, navigating model and experimental directions, engineering workable gradients, and posing the model
mathematically. Rosanne implemented preliminary experiments and multi-attribute control, and cleaned and
coordinated release of the code. Rosanne and Jason oversaw the project.
1

Table 1: The PPLM employs a pre-trained language model (LM) without any changes to the model
parameters and can generate text with controlled attributes such as topic and sentiment. We demon-
strate control with two tiny and easy to construct attribute models: a bag of words (BoW) related to a
topic and a linear discriminator trained on top of LM latent representations to control sentiment. The
underlined prex is what the LM is conditioned on to generate a passage of text (e.g.The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato).
The controlled attributes are colored and bracketed (e.g.[Science]), and words in the BoW that are
directly optimized for are highlighted brightly (e.g.). The softer highlights correspond to
words related to the attribute, but not directly optimized for during the control process (e.g.).
[–]The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato
challenge, try some garlic mashed potatoes.
[Negative]The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato. It can make you fat, it can cause you to have a
system, and it can even kill you.. . .
[Positive]The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato
I've always had a hard time keeping a recipe secret. I think it's the way our kids
little ones.
[Science]The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato
food source since the mid-1800s, but recent
researchers from Johns Hopkins University. . .
[Politics] To concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo conclude
inuential
building a
[Politics] To concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo conclude, the most signicant and lasting
2008 was that manys, including those in the
modern history.
generated text becomes difcult without modifying the model architecture to allow for extra input
attributes or ne-tuning with attribute-specic data (Keskar et al., 2019; Ziegler et al., 2019).
Controllable generation entails modelingp(xja), whereais some desired controllable attribute(s)
andxthe generated sample. However, generative models only learnp(x). In computer vision,
Plug & Play Generative Networks (PPGN) from Nguyen et al. (2017) developed a mechanism for
generating images with different attributes by plugging a discriminator (attribute model)p(ajx)
together with a base generative modelp(x)and sampling from the resultingp(xja)/p(ajx)p(x),
effectively creating a conditional generative model on the y from any supplied attribute model. In
a similar manner, we propose the Plug and Play Language Model (PPLM) for conditional language
generation that combines one or more simple attribute modelsp(ajx)—either in the form of a bag-
of-words (BoW) or single layer classiers—with a pre-trained, unconditional language modelp(x).
We sample from the resulting combined model by following gradients in the latent representation
space in a manner inspired by the approximate Metropolis-adjusted Langevin (MALA) (Roberts
et al., 1996; Roberts & Rosenthal, 1998) sampler deployed in Nguyen et al. (2017).
Optimization is performedex post factoin the activation space, thereforeno re-training or ne-
tuning is needed. Control is ne-grained, with a strength parameter determining how strong the
attribute inuence should be; a strength of0fully recovers the original modelp(x). This design
allows vast exibility: users can combine a state-of-the-art generative model, which may be large
and difcult to train, with any number of attribute controllers. Attribute models may be easier to train
or untrained (in the case of BoW models), and multiple controllers may be combined exibly during
inference. In this paper, we demonstrate the PPLM approach using a GPT-2 345M model (Radford
et al., 2019) as the general-purpose LMp(x), but the method applies in any representation space
from any transformer-based text generator and allows combination with any attribute modelp(ajx).
We demonstrate controlled generation with a number of attribute controllers, assembled and com-
bined during generation, each with a different strength, acting as a set of “control knobs” that tune
generation towards the desired attribute (see examples in Table 1). Code for the models and exper-
iments is available at:https://github.com/uber-research/PPLM . Our key contribu-
tions are:
•
to existing work, and how sampling from a PPLM works (Sections 2 and 3).
2

•
each dened using a bag of words, and 1 simple discriminator on sentiments. We quantify
effectiveness using both automated evaluation (separately trained perplexity and sentiment
models) as well as human evaluation (for attribute relevance and uency). All evaluations
point toward the ability of PPLMs to generate attribute controlled, uent text (Section 4).
•
2 netuned for positivty (Ziegler et al., 2019). Our method, without any LM training, is
on par and often outperforms the baselines on attribute relevance and uency (Section 4.2,
and Section 4.3).
•
tion of toxic content is likely by following the negative gradient of a model trained to detect
toxicity (Section 4.4). We also show how PPLM can be used for structurally constrained
story writing (Section 4.5).
2 RELATEDWORK
Controlled generationCurrent methods for controlled text generation involve either ne-tuning
existing models with Reinforcement Learning (RL) (Ziegler et al., 2019), training Generative Ad-
versarial Networks (Yu et al., 2017), or training conditional generative models (Kikuchi et al., 2016;
Ficler & Goldberg, 2017). Different from our approach, these methodologies are not plug and
play, since the entire model needs to be separately ne-tuned for each specic attribute. Keskar
et al. (2019) train a large language model with over 50 different control codes. The results are high
quality because they train exactly to maximizep(xja), but this comes at the expense of xing control
codes up front and of training a very large model (1.6B parameters). Our method does not require
retraining any conditional generative model, and both the language model and the conditional model
can be exibly assembled. Table 2 gives a comparison of recent approaches to language modeling
tuned for specic attributes. In another interesting but tangential piece of work, Subramani et al.
(2019) recently showed that a pre-trained language model can be steered to recover arbitrary sen-
tences. Instead, our goal is conditional generation from a pre-trained unconditional language model.
Noisy Channel ModelingYu et al. (2016), and more recently Yu et al. (2019); Yee et al. (2019);
Ng et al. (2019), leveraged the Shannon Noisy Channel Theory (Shannon, 1948) for improving
sequence-to-sequence modeling. Their approach translates a source language sentenceyinto a target
language sentencexby rst sampling from a forward model proposal distributionpforward(xjy)and
then reranking samples based on probabilities given bypbackward(xjy)/p(x)p(yjx). PPLM scores
samples using the same basic equation, but as we have no forward or proposal modelpforward(xja),
we rely on the latent space updates proposed by Nguyen et al. (2017). As a baseline, we consider
usingp(x)as a “forward model” and then reranking, which we will see works moderately well in
some scenarios and poorly in others (see Tables 4 and 6).
Weighted decodingHoltzman et al. (2018); Ghazvininejad et al. (2017) consider controlled lan-
guage generation – the former with discriminators, and the latter with a bag of words – where the
decoding procedure is modied to consider the scoring function used for decoding. See et al. (2019)
note that control with weighted decoding (WD) is difcult and often leads to sacricing uency and
coherence. Further, Ghazvininejad et al. (2017) strongly relies on sampling from a set of keywords
on a specic topic and it does not allow to bias generation towards a topic in a manner that does not
necessary include a set of keywords. Similarly, Baheti et al. (2018) proposed a decoding strategy
for generating interesting responses in dialogue systems, using bags of words and word embed-
dings. Sophisticated sampling methods(Metropolis et al., 1953) can be used to constrain the model
generation to certain keywords and topics. We evaluate WD as a baseline.
Text Style TransferOutside of language modeling, the eld of text style transfer performs a re-
lated task. Shen et al. (2017); Hu et al. (2017) train variational auto-encoders for style transfer that
rely on learning disentangled latent representations for style and content. Li et al. (2018) demon-
strate the efcacy of a simple approach based on replacing attribute related n-grams with n-grams
corresponding to the desired attribute based on a conditional generative model. A key difference
3

Table 2: Comparison of the different models and distributions under consideration. All models in
this table are useful in different scenarios. The particular advantage of PPLM is that very small,
custom attribute models,p(ajx), may be combined with powerful, general pre-trained language
models,p(x), to create cheap but still powerful conditional generative models,p(xja).
Model type Form of model Samples
Example models
and number of trainable params
Language Model p(x) Uncond.
GPT-2 medium: 345M
(Radford et al., 2019)
Fine-tuned
Language Model
p(x) Uncond.
Fine-tuned GPT-2 medium: 345M
(Ziegler et al., 2019)
Conditional
Language Model
p(xja) Cond.
CTRL: 1.6B
(Keskar et al., 2019)
Plug and Play
Language Model
(PPLM)
p(xja)/p(x)p(ajx)Cond.
PPLM-BoW: 0 (curated word list)
PPLM-Discrim: 4K
(not counting pretrainedp(x))
between the above and our approach is that we use an ofine discriminator and perform optimiza-
tion based on this discriminator, which as suggested by Elazar & Goldberg (2018) may outperform
adversarial training approaches. More recently, Lample et al. (2019) adapt an approach from un-
supervised language translation to style transfer, where a denoised auto-encoder is trained with an
objective consisting of a weighted combination of a re-construction loss and a back-translation loss.
While the above approaches have shown impressive success on style transfer tasks, the main focus
is not controlled language generation, and further, the methods are notplug and play.
3 PLUG ANDPLAYLANGUAGEMODELS
3.1 LANGUAGEMODELING WITHTRANSFORMERS
Given a sequence of tokensX=fx0;


; xng, LMs are trained to compute the unconditional prob-
ability of the sequencep(X). This probability can be rewritten in terms of product of conditional
probabilities by recursively applying the chain-rule (Manning et al., 1999; Bengio et al., 2003) as:
p(X) =
n
Y
i=1
p(xijx0;


; xn1) (1)
In this paper, we use a transformer (Vaswani et al., 2017) to model the distribution of natural lan-
guage. To present our approach clearly, we rst briey summarize the transformer using recur-
rent notation. Let us dene the history matrixHtto consist of the key-value pairs from the past
i.eHt= [(K
(1)
t; V
(1)
t);


;(K
(l)
t; V
(l)
t)], where(K
(i)
t; V
(i)
t)corresponds to the key-value pairs
from thei-th layer generated at all time-steps from 0 tot. Efcient implementations of the trans-
former (Wolf et al., 2019) use the cachedHtto generatext+1, givenxt. This recurrent interpretation
of a transformer can be summarized as:
ot+1; Ht+1=LM(xt; Ht); (2)
xt+1pt+1=Softmax(W ot+1); (3)
whereWis a linear transformation that maps the logit vectorot+1to a vector of vocabulary size.
This allows for efcient language generation without repeated forward passes corresponding to the
prior conditioning textx0; : : : ; xt1.
3.2 STEERING GENERATION :ASCENDINGlogp(ajx)
In order to control the output of the language model, at every generation stept, we shift the history
Htin the direction of the sum of two gradients: one toward higher log-likelihood (LL) of the attribute
aunder the conditional attribute modelp(ajx)and one toward higher LL of the unmodied language
modelp(x). Combining these factors with a variable multiplier provides us with a controllable
“knob” to guide generation in a given direction with a specied strength. The updates are restricted
toHtand not the other model activations because future predictions depend on the past only viaHt
4

LM LM LM
Attribute Model p(a|x)
The chicken tastes
chicken tastes Grad
(Positive
sentiment)
ok delicious
Original distribution
("ok")
Updated distribution
("delicious")
Updated Latents
Backward Pass
and update latents
Forward Pass
Recompute with
updated latents
p(x)p(x)p(x)
Recompute
Step 1
{
{
{
Step 2
Step 3 Figure 1: Simplied illustration of the proposed approach in three phases. In Step 1, a forward pass
is performed through the language model to compute the likelihood of a desired attribute using an
attribute model that predictsp(ajx). In Step 2, a backward pass updates the internal latent represen-
tations of the LM, using gradients from the attribute model, to increase the likelihood of the passage
having the desired attribute. In Step 3, a new distribution over the vocabulary (ept+1) is generated
from the updated latents(
e
Ht)and the current tokenxt. The next token is then sampled from the
updated distribution. This process of updating the latents is repeated at each time-step, leading to
a gradual transition towards the desired attribute. For computational efciency, one may choose to
modify only the latents within some window of the recent past, depicted as the dotted-red region.
(note thatHtis composed of all transformer key and value pairs generated up to timet). Taking
steps inHtspace leads to gradual changes to model activations — which may be thought of as
gradual reinterpretations of the past — that guide future generation in the desired direction.
Let
Htbe the update toHt, such that generation with(Ht+
Ht)shifts the distribution of
the generated text such that it is more likely to possess the desired attribute.
Htis initialized
at zero and updated with gradients from an attribute model that measures the extent to which the
generated text possesses the desired attribute (e.g. positivity). We rewrite the attribute modelp(ajx)
asp(ajHt+
Ht)and then make gradient based updates to
Htas follows:

Ht
Ht+
r
Ht
logp(ajHt+
Ht)
kr
Ht
logp(ajHt+
Ht)k

(4)
whereis the step size,is the scaling coefcient for the normalization term.
1
This update step
can be repeatedmtimes; in practice we use3to10. Subsequently, a forward pass through the LM
with the updated key-value pairs is performed to obtain the updated logitseot+1aseot+1; Ht+1=
LM(xt;
e
Ht), where
e
Ht=Ht+
Ht. The perturbedeot+1is then used to generate a new distribution
ept+1as in Equation 3.
3.3 ENSURING FLUENCY :ASCENDINGlogp(x)
The approach described in the previous section is able to generate text tuned for a particular dis-
criminator, but left unchecked it will quickly result in unrealistic adversarial or fooling examples
(Szegedy et al., 2013; Nguyen et al., 2015) as the text moves into low probability regions. To com-
bat this, we use the unconditional language model in two ways that ensure the uency is maintained
at or near the level of the unconditional language model (here GPT-2).
Kullback–Leibler (KL) DivergenceWe update
Htto minimize the KL divergence between the
output distribution of the modied and unmodied language models in addition to the step above.
In practice, this is accomplished by adding the quantities together before taking a gradient, though it
can be visualized as two separate steps as in Figure 2. We scale the KL coefcient by a scalarKL,
and in practice, setting this hyperparameter to 0.01 works well in general across tasks.
1
One normalization term is computed for each layer of the transformer.
5

Figure 2: An oversimplied, Markov chain view
into why steps that maximize bothlogp(ajx)and
logp(x)are needed. The sentence under consider-
ation is shown as a black dot, which is rst pushed
in the direction of maximizinglogp(ajx)and then
in the direction of maximizinglogp(x). In prac-
tice we use a single step and simply add the log
probabilities; we take steps in continuous space of
hidden representationsHrather than in the dis-
cretex(byte pair) space, and rather than resam-
pling the entire sentence each step, we take one
step inHspace per byte-pair sample. p(x)
lowe r
highe r
p(a|x)
lower higher
ascend p(a|x)
ascend p(x)
Post-norm Geometric Mean FusionIn addition to minimizing KL divergence, which affects
the past via
Ht, we performpost-norm fusionsimilarly to Stahlberg et al. (2018). This does not
directly affect
Ht; rather, it just serves to constantly tie the generated text to the unconditionalp(x)
LM distribution. We accomplish this by sampling fromxt+11=

ep
gm
t+1
p
1gm
t+1

, wherept+1
andept+1are the unmodied and modied output distributions, respectively, andis a normalizing
factor such that it forms a valid distribution. Asgm!1this converges to the distribution from
the updated LM, and asgm!0it converges to the unconditional LM distribution. We nd that in
practice values forgmin the range0:80:95work well.
3.4 SAMPLING ANDRANKING
The attribute modelp(ajx)in PPLM provides two functionalities: rst, a score that can be used to
rank samples based on the LL of the desired attribute (forward pass only; Step 1, Figure 1), and
second, a gradient ascent direction to perform an update in the latent space (Step 2 & 3; Figure 1).
The former can be used to generatersamples and rank them to choose the best one. This can
serve as an additional method for attribute control in addition to sampling with updated latents.
Further, to avoid the problem of repetitive, low quality text (Holtzman et al., 2018), we compute the
mean over the Dist-1, Dist-2 and Dist-3 scores (for the generated passage), which is an indicator of
repetitiveness (Li et al., 2015), and then discard samples with a mean score below a threshold.
4 EXPERIMENTS, RESULTS,ANDEVALUATION
In this section we describe our evaluation methodology and then show controlled generation results
under various attribute models. We also show use cases of PPLM in language detoxication and in
controlled story telling. For all results reported in this section, we use top-k sampling (Fan et al.,
2018) withk= 10to draw from the softmax distribution over the vocabulary.
4.1 EVALUATION METHODS AND ABLATION STUDY
We evaluate to assess two properties: whether PPLM generates text that satises the desired attribute
(topic or sentiment) and whether the quality of its text deteriorates as we intensify control of the
attribute. Note we can always turn the control knob down to zero to disable control of attributes and
reach the uency of the original model. If desired, a user can tune the knobs at inference until a
chosen tradeoff between attribute strength and uency is reached. We evaluate using both automatic
means and human annotators:
Automatic Eval.Perplexity is an automated measure of uency, though its effectiveness has been
questioned in open-domain text generation (Liu et al., 2016). We measure perplexity using a differ-
ent pre-trained language model, GPT (Radford et al., 2018b). The diversity of text in the passages
is measured using the number of distinct n-grams (normalized by the length of text) as in Li et al.
(2015). We report Dist-1, Dist-2, and Dist-3 scores for the distinct 1-2-3-grams (measured across
all samples generated for a given attribute control task, e.g. a specic topic for topic control). Such
6

Table 3: Comparison of different samples generated by (top row) baseline GPT-2 and (other rows)
PPLM with different BoW corresponding to different topics (e.g.[Military]), all conditioned on
a single prex: "The issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focused". Both directly optimized (in) and related words (in
red) are highlighted, showing how the optimization takes effect. Note that sometimes related words
appear before directly optimized words, showing the subtle effect of control.
[–]The issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focused
Michael Brown in Ferguson, Mo., Eric Garner in New York City and Sandra Bland in Texas, as well as the
shooting of unarmed teen Michael Brown by a white police ofcer in Ferguson, Mo. A grand jury declined
to bring charges against the ofcers and released the dashcam videos that showed. . .
[Military]The issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focused
could not
airspace The
[Space]The issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focused
alleged attack by Islamic State ghters on a Kurdish checkpoint, the use of
technology research by Russian and American The. . .
[Science]The issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focused
has puzzled many, who have attempted to
mechanics, but they have to
[Politics]The issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focused. It's unclear whether the
vote, but the "The issue of the's
applicability to the's
[Computers]The issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focused
engagement in the
media According to a report by
Monitor
surpassed
scores are an indicator of the diversity of the samples generated (Li et al., 2015). We aslo use external
sentiment classiers for sentiment evaluation.
Human Eval.We consider two types of human annotation: uency and A/B testing on attribute
relevance. Annotators are asked to evaluate the uency of each individual sample on a scale of 1-5,
with 1 being “not uent at all” and 5 being “very uent,” as done in Lample et al. (2019). In the A/B
testing for attribute relevance, we consider all combinatorial pairs of all four variants: B, BR, BC,
and BCR (6 combinations). We then ask annotators to rank the pair on the desired attribute (e.g. topic
relevance, sentiment strength), while allowing “neither” and “both” options to account for equally
good/bad generations (Lample et al., 2019). We obtain annotations from nine external occupational
annotators. Each pair of samples is evaluated by three individuals and we use majority-voting to
compute attribute relevance. For uency we use average of the three annotations. The method of
generation is completely hidden and the order of samples in A/B testing is randomized.
Ablation study and baselines.We conduct an ablation study with four variants:B: the baseline,
unchanged GPT-2 LM, sampled once;BR: B but sampledrtimes, with best sample chosen based
on the LL ranking and ltering based on Dist score;BC: update the latent representations(
e
Ht)and
then sample once; and lastlyBCR: update the latent representations(
e
Ht)and generatersamples,
choose the best sample based on the LL score (after ltering out samples with low Dist scores). As
baseline approaches we considerCTRL: (Keskar et al., 2019), a recent language model;GPT2-
FT-RL: a GPT-2 LM ne-tuned for human evaluated positivity with RL (Ziegler et al., 2019); and
WD: a weighted decoding baseline in which the B model's outputs are weighted directly toward
maximizingp(ajx)(Ghazvininejad et al., 2017); see Section S6 for details. Hyperparameters used
for each experiment are given in Section S10
4.2 BOWATTRIBUTE MODELS
The simplest attribute model we use gives the log of the sum of likelihoods of each word in some
predened Bag of Words (BoW). Given a set of keywordsfw1;


; wkgthat specify a topic of
7

interest and the output distribution of the language modelpt+1, the log likelihood is:
logp(ajx) = log
k
X
i
pt+1[wi]

(5)
We construct BoWs that represent seven distinct topics:SCIENCE,MILITARY,LEGAL,COMPUT-
ERS,SPACE,POLITICS, andRELIGION(see Section S16 for complete word lists). Samples are
shown in Table 3, generated from a single prex, while being controlled towards each topic. Inter-
estingly, we nd that increasing the probability of generating the words in the bag also increases
the probability of generating related topical words not in the BoW (e.g. in the[Science]sample
shown in Table 3, note that).
Table S17 shows the gradual change of topic intensity under ne-grained control. We found that
the optimization procedure works better with updating representations from the past over a nite
window and using an adaptive normalization scheme (see Section S10.3).
For automatic and human evaluation, we generate 420 samples evenly distributed among seven BoW
attribute models and 20 prexes (see the full list in Section S14), for each of the four variants de-
scribed in the ablation study. See Section S7 for further details on evaluation and results. Table 4
show that human annotators nd text from BCR (51.7%) and BC (46.9%) to be signicantly more
on topic than B (15.8%) and BR (11.1%). With only a slight degradation in uency scores, passages
generated with manipulated latents (BCR and BR) are signicantly on topic, demonstrating the de-
sired attribute control on this task. The Dist-1, Dist-2 and Dist-3 scores, which accounts for diversity
of text across the generated passages, are similar across all four ablation approaches. Further, BCR
slightly outperforms CTRL (51.7% & 50.0%), and signicantly outperforms WD (36 %). It is also
interesting that BC itself outperforms WD (36 %). BCR, CTRL and WD all score similarly on the
uency metric.
We note that gradient-based latent updates have signicantly greater inuence on topic relevance
(R with or without C) than reranking based on the score (C with or without R), showing that shift-
ing meaning in latent space is more effective than shifting the output distribution directly through
reweighting. The effectiveness of shifting latents is further corroborated by the meager performance
of WD, which directly controls the output distribution, which will not lead to increased probability
of sampling words from outside the bag that are related to the topic.
Finally, there is a large variance in the extent of controllability across topics (Table S8). We nd
that some topics (religion, science, politics) are easier to control for compared to others (comput-
ers, space). Section S8 considers unusual or nonsensical combinations of prexes and attributes
(e.g. prex `potato' and topic 'religion'), and we nd that even for these settings PPLM is able to
successfully control for the desired attribute, often with hilarious twists!
4.3 DISCRIMINATOR ATTRIBUTE MODELS
While BoW models have been demonstrated to be able to control text attributes such as sentiment
(e.g., Li et al. (2018) rely on extracting a set of attribute-based phrases to control the sentiment
during style transfer), being able to control attributes using more sophisticated discriminators is
desirable when it is difcult to express the attribute with a simple bag of words.
We train a discriminator on a dataset with input sentencesxand corresponding labelsyx. For an
inputxof lengtht, we computeo
x
:tand trainfon the mean (o
t
) of the embeddings across time. All
discriminators in this work consist of a single layer classier that predicts the target label fromo
x
t.
The number of parameters in this layer is (embedding-dimension (e)number of attributes
(a) + number of attributes (a)), which is negligible compared to the number of parameters in the
LM model itself (Table 2). Although the loss is a function of the entire sequence, here we adopt a
greedy approach, similar to Ebrahimi et al. (2018); Wallace et al. (2019), in which we optimize for
a higher-probability of the sequence having a specic attribute by considering changes only to the
next token to be generated. This objective can be described as follows, wherefis the discriminator:
logp(ajx) = logf(o:t+1; ot+2): (6)
Note thatot+2is a function ofxt+1. Further,xt+1Softmax(W~ot+1), which depends on
Ht.
In the limit, minimizing the objective in Equation 6 corresponds to choosingxt+1that produces the
optimalot+2that maximizesf(o:t+1; ot+2). However, this limits the diversity of the generated text
8

Table 4: For each treatment in the ablation study, we report meanstd-dev across (human and au-
tomated) uency metrics. The topic (%) reports the fraction of samples matching the target topic,
as evaluated by human annotators. Table S8 provides per-topic results. Approaches BC and BCR
demonstrate signicant control over the topic of the generated text, while retaining similar diversity
(Dist-1, Dist-2, Dist-3) scores and minimal degradation in Perplexity and Fluency evaluations vs the
baseline LM (B). The gain from ranking and choosing from multiple samples BR over B is limited
(4.7%). The gain in topic-accuracy from latent (
e
Ht) manipulation (from B to BC) is signicantly
higher (35.8%). Perplexity is computed using the GPT LM (Radford et al., 2018a), which differs
from the LM generating text (GPT-2). For CTRL and WD, since human evaluation is performed
in comparison with BCR via A/B testing, we report the numbers for BCR as well from these com-
parisons, for the human evaluated metrics. Further, we consider one sample per prex for CTRL,
resulting in fewer samples and higher Dist-1, 2, 3 scores as a consequence. PPLM outperforms
CTRL and WD on topic-relevance, while being comparable on uency scores.
Method Topic % ("better) Perplexity Dist-1 Dist-2 Dist-3 Fluency ( "better)
(human) ( #better) ("better) ("better) ("better) (human)
B 11.1 39.85 35.9 0.37 0.79 0.93 3.60 0.82
BR 15.8 38.39 27.14 0.38 0.80 0.94 3.68 0.77
BC 46.9 43.62 26.8 0.36 0.78 0.92 3.39 0.95
BCR 51.7 44.0425.38 0.36 0.80 0.94 3.52 0.83
CTRL 50.0 24.48 11.98 0.40 0.84 0.93 3.63 0.75
BCR 56.0 – – – – 3.61 0.69
WD 35.7 32.05 19.07 0.29 0.72 0.89 3.48 0.92
BCR 47.8 – – – – 3.87 0.71
and could potentially lead to language degeneration (Holtzman et al., 2019). Alternatively, we focus
on a softer optimization approach where we aim to shift the distribution~pt+1=Softmax(W~ot+1)
towards one that in expectation has a higher likelihood of having the desired attributea. Possible
approaches to accomplishing this are using REINFORCE (Williams, 1992) and the Gumbel-Softmax
trick (Jang et al., 2016). However, both of these would slow down convergence. Instead, as in Dai
et al. (2019a), we use the distribution~pt+1(instead of a hard samplext+1), and feed it forward to
the generate the next embeddings token and update
Ht.
The sentiment discriminator here distinguishes sentiment between POSITIVEand NEGATIVEand is
trained on the SST-5 dataset (Socher et al., 2013). Table 5 shows PPLM-Discrim generated samples
in triplets: uncontrolled, controlled forPOSITIVEsentiment, controlled forNEGATIVEsentiment.
For automatic and human evaluation, we use 15 prexes (see the full list in Section S14) to generate
45 samples for each of two sentiment classes:very positiveandvery negative. Note
that even though the sentiment discriminator is trained with movie review data, the prexes (e.g.
“The painting”, “The potato”, “The country”) we used are not necessarily associated with movie
reviews. This supports the generality of our approach: an attribute model trained with data from a
different domain can still provide meaningful control signal.
Table 6 shows evaluation results. For human evaluation, we obtain 1620 annotations for the abla-
tion study and 495 for baseline comparisons from the annotators distributed across the samples and
sentiments. Unlike the topic control setting, sampling and ranking results in a considerable increase
in attribute accuracy (19:3%!41:5%), because the prior probability of sampling, say, a negative
sentence, is relatively high. BC results in a decrease in uency when compared to B, while being
signicantly more consistent with the desired attribute (19:3%!39:6%). With latent manipulation
and ranking (BCR), we see a signicant increase in attribute control accuracy (73:7%) while retain-
ing uency similar to B and BR. Further, the gain in sentiment accuracy from re-sampling is larger
in the case of manipulated latents vs non-manipulated (34:1%increase from BC to BCR>22:2%
increase from B to BR), indicating that these two approaches may be protably combined. We also
evaluate attribute control with an external sentiment classier trained on IMDB movie reviews (Maas
et al., 2011), which is a different dataset from the one used to train the attribute model (Socher et al.,
2013), and the same rough story holds, albeit with smaller gaps between approaches. We compare to
baselines CTRL, GPT2-FT-RL, and WD. BCR performs comparably to CTRL (73.7% and 80.0%),
and BR, BC and BCR all outperform GPT2-FT-RL, the GPT-2 LM ne tuned for positivity, and WD.
9

Table 5: Sentence samples in triplets, generated by {baseline GPT-2, PPLM-DiscrimPOSITIVE,
PPLM-DiscrimNEGATIVE}, conditioned on prexes:The chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chicken The countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe country. Words related to
the sentiment are highlighted (in). Each triplet is generated from the same random seed.
[-]The chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chicken The city has released an image of a proposed development in the
city of Portland's West End.. . .
[Positive]The chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chicken,
cooked. The only thing to say is that the sauce was, and I think that the broth really complemented
all of the other avors. The
[Negative]The chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenpox. The United States is
facing one of the
[-]The countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe country's new chief minister, A.J. Paik, is a member of a group of prominent conservative politicians
who have criticized the Obama administration's efforts to. . .
[Positive]The countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe country's largest indoor painting event!Come
outdoor murals, a
[Negative]The countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe countryThe country's top, rather than a toilet, to
ush their, an ofcial at a
major
4.4 LANGUAGEDETOXIFICATION
Language models trained with large corpora of Internet data reect biases and discrimination ex-
isting in the data. A recent paper by Wallace et al. (2019) conducted adversarial attacks that make
GPT-2 produce racist output when given a carefully optimized trigger string as prex. They also
nd that when simply using “Blacks” as prex, 2% of GPT-2 samples contain explicit racism. Other
prexes (e.g., “Asians” or “Jews”) are mentioned but no percentage is reported. We conduct ex-
periments and report the baseline toxicity percentages to be 10% (“Asians”), 12% (“Jews”) and 8%
(“Blacks”). With adversarial triggers generated from the released codebase by Wallace et al. (2019)
the average toxicity percentage is 63.6%. Further details can be found in Section S12.
PPLMs can be easily adapted for language detoxication by plugging in a toxicity classier as the
attribute control model and update latents with the negative gradient. We train a single layer classier
on the toxicity data from the Toxic Comment Classication Challenge(jig) and show that with a
similar hyper-parameter setting as other PPLM-Discrim methods, it works well on both natural
prompts and adversarial triggers. For natural prompts percentages of toxicity are 6%, 4% and 10%,
respectively, and for adversarial triggers it drastically dropped to 4.6% on average, with statistical
signicance. Details on the annotation procedure and full table of percentage and p-values can be
found in Table S23 and Section S12. Note that a model for detoxifying language can also potentially
be maliciously used for generating toxic language, a topic we briey discuss in Section 5.
4.5 CONTROLLEDSTORYWRITING
We explore controlled generation for assistive story writing (Peng et al., 2018; Luo et al., 2019; Yao
et al., 2019; Fan et al., 2018). Using an uncontrolled LM for assistive art creation can be difcult
because of the content deviating from the desired topic and becoming incoherent. To help with
the structure, we use predened story skeletons often used in improvisation (Adams). We ll in the
blank between these prexes with a PPLM. See examples in Table S20 and Table S21.
5 DISCUSSION
We present PPLM, aplug and playmethod for controlled language generation that allows exible
assembling of a large, pre-trained language model and a BoW or a small, easy-to-train discriminator,
and achieves ne-grained control of attributes such as topics and sentiment. Without retraining or
ne-tuning the language model, the simple mechanism shows great capability of attribute control
while retaining uency. We believe this method could serve as a simple baseline for the largely
open-ended language generation tasks where controlling is challenging.
There has recently been a substantial discussion around the ethics of capable language models (Rad-
ford et al., 2019; Keskar et al., 2019), both in their potential to recapitulate problematic social biases
10

Table 6: Evaluation of models and variants on the sentiment control task, with meanstd-dev
reported across various uency metrics. Sentiment accuracy reports the fraction of samples with
the target sentiment as evaluated by humans or an external classier. Approach BCR provides
signicant control over sentiment while showing minimal degradation in uency. See Table S9 for
full results on individual sentiments. *GPT2-FT-RL is only evaluated for the positivity half of the
task, as it is ne-tuned only for positivity (Ziegler et al., 2019). For human evaluation metrics, we
compare the baselines CTRL, GPT2-FT-RL and WD with BCR and perform A/B style testing. We
include both numbers from the comparison.
Method Sentiment Acc. (%) Sentiment Acc. (%) Perplexity Dist-1 Dist-2 Dist-3 Human Evaluation
(human) (external classifer) ( #better) ("better) ("better) ("better) Fluency ("better)
B 19.3 52.2 42.1 33.14 0.37 0.75 0.86 3.54 1.08
BR 41.5 62.2 44.6 34.72 0.37 0.76 0.87 3.65 1.07
BC 39.6 64.4 41.8 34.87 0.33 0.70 0.86 2.79 1.17
BCR 73.7 78.8 46.640.24 0.36 0.77 0.91 3.29 1.07
CTRL 76.7 96.6 37.4 16.89 0.35 0.78 0.89 3.54 0.77
BCR 70.0 – – – – – 3.36 0.82
GPT2-FT-RL* 13.3 77.8 217.3 176.4 0.54 0.91 0.94 3.31 0.84
BCR 84.4 – – – – – 3.68 0.83
WD 18.9 52.2 31.7 28.0 0.33 0.69 0.83 3.67 0.89
BCR 61.1 – – – – – 3.75 0.66
and for them to be directly abused for societal harm (e.g. to generate disinformation). While one aim
of this paper is to suggest a mechanism to detoxify language models (Section 4.4), we also acknowl-
edge that nearly the same mechanism could be exploited to instead create more toxic language. Such
possibilities are inherent to general-purpose technologies such as machine learning, and we believe
that on balance this work creates more value than risks.
AcknowledgementsThe authors gratefully thank Bryan McCann for providing samples for the
CTRL baseline, Joel Lehman for discussion regarding the ethical implications for this work, Jiale
Zhi for help with the computational framework, Colan Chen for creating associated artwork for
the blog, Julien Chaumond, Lysandre Debut, Thomas Wolf, and the Hugging Face team for co-
producing the PPLM demo and helping integrate the code into their transformers repository, all the
annotators at Uber for their labeling, and members of the Deep Collective research group at Uber
AI for helpful discussion, ideas, and feedback on experiments.
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14

SUPPLEMENTARY INFORMATION FOR :
PLUG ANDPLAYLANGUAGEMODELS:ASIMPLE
APPROACH TOCONTROLLEDTEXTGENERATION
S6 DETAILS ONBASELINE METHODS
We consider three baselines: CTRL, GPT2-FT-RL, and WD. The rst two are strong baselines where
large language models are trained (or ne-tuned) specically to generate texts conditioned on certain
attributes, while WD is considered a weak baseline based on a direct integration of the conditioning
into the decoding.
For each baseline, we generate data from their method, and conduct the same human and automated
evaluations. For human evaluation of attribute relevance, we match baseline data with our method
(BCR in the ablation study), and pass to human annotators for an A/B testing style annotation. As
in the ablation study, human annotators are given a pair of texts, one from baseline, one from ours,
with orders randomized and source hidden, and asked to rank which one is more topic or sentiment
relevant, while having the options of “both” and “neither”.
On top of that, we have human annotators to give the uency score of each text sample under
each method individually. And automated evaluations of perplexity, sentiment, etc. are also done
individually.
S6.1 CTRL
The recent conditional language model, CTRL, from Keskar et al. (2019), trains a 1.6B LM condi-
tioned on around 50 control codes. We use the ofcial released codebase
2
and their open-sourced
model to generate samples for the CTRL baseline. Out of the 7 topics considered in PPLM-BoW,
we found that 5 can be matched with a specic control code in CTRL. We append a secondary
code "Text:" to each primary control code, per the author's suggestion, to encourage more uent and
longer passages. The 2 topics missing a match with CTRL are: Military, Space. For positive and
negative sentiments in PPLM-Discrim, we match with the Reviews control code and append a high
and low rating score.
The matched attributes and control codes are listed in Table S7.
Under this setting, for each control code we generate texts prompted by the same prexes used for
corresponding PPLM attribute model (20 for PPLM-BoW, 15 for PPLM-Discrim). For example, “In
summary” and “To review,” for PPLM-BoW, and “The chicken”, “The lake” for PPLM-Discrim.
Due to the near-greedy sampling method CTRL uses, for each prex it generates one sample. Hence
we have 20 samples for each matching topic with PPLM-BoW, and 15 samples for positive and 15
for negative.
Table S7: Control codes used for the model from Keskar et al. (2019) for experiments in Section 4.
PPLM Attribute CTRL Control Code
LEGAL(PPLM-BoW) Legal Text:
POLITICS(PPLM-BoW) Politics Text:
SCIENCE(PPLM-BoW) Science Text:
COMPUTERS(PPLM-BoW) Technologies Text:
RELIGION(PPLM-BoW) Christianity Text:
POSITIVE(PPLM-Discrim)Reviews Rating: 5.0
NEGATIVE(PPLM-Discrim)Reviews Rating: 1.0
2
CTRL codebase:https://github.com/salesforce/ctrl
15

S6.2 GPT2-FT-RL
A recently released GPT-2 model ne-tuned using human feedback, from Ziegler et al. (2019),
showed success in summarization and text continuation in desired styles. To compare with PPLM,
we run GPT2-FT-RL
3
to generate positive texts on the same prexes used in our PPLM-Discrim
experiment. For each prex, we generate three GPT2-FT-RL samples, and pair them with those
generated from PPLM (BCR in the ablation study) randomly.
S6.3 WEIGHTED DECODING (WD)
We consider a simple baseline based on a direct integration of the conditioning into the decoding
procedure, similar to the approach from Ghazvininejad et al. (2017).
Topic Control with Bag of WordsIn Ghazvininejad et al. (2017), the authors consider increasing
the likelihood of sampling from a bag of key-words by performing beam-search with a modied
scoring function.
score(wi; bt) =score(bt) +logPt+1(wi) +
X
i
1BoW(wi);
where1BoW(wi)is an indicator function indicating if the tokenwiis present in the bag BoW. Since,
it has been shown that beam-search results in degradation of language for GPT-2 (Holtzman et al.,
2019), we consider top-5 sampling from a distribution~pt+1dened such that:
~pt+1(wi) =pt+1(wi) +1BoW(wi)pt+1(wi)
where2R++andpt+1is the distribution over the vocabulary as predicted by the GPT-2 LM . For
the experiments in Section 4, we set= 10.
Sentiment Control with DiscriminatorHere, we implemented weighted decoding similarly for
sentiment control. Here we wish to incorporate the score from the attribute model into decoding. To
control for style^a, instead of sampling from the distributionpt+1, we sample from~pt+1dened as:
~pt+1(wi) =p(a= ^ajx0:t; wi)pt+1(wi):
p(a= ^ajx0:t; wi)is the probabilty of the sequencex0:t; wipossessing attribute^aas assigned by the
attribute model. By Bayes' rule,p(a= ^a;wijx0:t) =p(a= ^ajx0:t; wi)pt+1(wi), and we do top-5
sampling from this distribution. Recall thatpt+1(wi) =p(wijx0:t)under the language model.
S7 FURTHER DETAILS ON HUMAN AND AUTOMATED EVALUATION
We conduct evaluations on attribute relevance and language uency, both including human and
automated evaluation.
For topic relevance (a.k.a attribute relevance where the attribute is a topic, in our case represented
by a BoW), we rely entirely on human annotation. For sentiment relevance, we rely on human
annotation as well as a separately trained sentiment classier. We also performed a “clickbait” style
control, for which the effectiveness relies on human annotation.
For uency, we use human annotations (between 1 to 5) and automated methods: perplexity, Dist-1,
Dist-2, and Dist-3 scores.
The number of human evaluations are as below:
•PPLM-BoW. For the ablation study, we have 20 prexes7 topics6 combinations
3 samples3 labels each, resulting in 7560 total annotations. For baseline comparisons,
we have (20 prexes5 topics) for CTRL and (20 prexes7 topics3 samples) for
WD, each then with 3 labels, resulting in 1560 total annotations.
3
GPT2-FT-RL codebase:https://github.com/openai/lm-human-preferences
16

•PPLM-Discrim, sentiments. For the ablation study, we have 15 prexes2 sentiments
6 combinations3 samples3 labels each, resulting in 1620 total annotations. For
baseline comparisons, we have (15 prexes2 sentiments) for CTRL and (15 prexes
3 samples) for GPT2-FT-RL and (15 prexes3 samples2 sentiments) for WD which
each have 3 labels, resulting in 495 total annotations.
•PPLM-Discrim, clickbait. We include in this section an additional discriminator attribute
model, clickbait classier. For this we use the same setting as sentiment, 15 prexes6
combinations3 samples3 labels each, resulting in 810 annotations.
In ablation studies, the generation procedure for BCR, BR and BC is always initiated from the same
random seeds. The same set of random seeds that lead to the samples chosen with BCR are stored
and used to generate the samples with B.
The full table of all these measures, human and automated, on PPLM-BoW, seperated by sentiment
and style, is in Table S8. Included also are strong baselines (CTRL and WD) for each sentiment.
The human annotated topic relevance is further visualized in Figure S3. The uency scores, while
being across {B, BC,BR, BCR,} methods in the table, when shown in distribution are very similar,
as seen in Figure S5.
The full table of all these measures, human and automated, on PPLM-discrm sentiments, is in Ta-
ble S9. Included also are strong baselines (CTRL, WD and GPT2-FT-RL) for each topic. The human
annotated sentiment and style (e.g. “Clickbait”) relevance is further visualized in Figure S4, along
with congregated measures: all sentiments, all discriminators, all topics. The uency scores again
have similar distributions across {B, BC,BR, BCR,} methods, as seen in Figure S6.Computers Legal Military Politics Religion Science Space
0
10
20
30
40
50
60
70
percent on topic
baseline (B)
baseline+reranking (BR)
gradient (BC)
gradient+reranking (BCR)
Figure S3: Topic relevance by human evaluation. We can see that taking a PPLM gradient step
(B!BC) makes a big difference. Reranking is mostly helpful (B!BR; BC!BCR). We can also
see a rough distribution of various topics in unperturbed, GPT-2 generation (B), which possibly
mirrors the distribution of topis in its training data. Some topics, like science, naturally appear
rather frequently.Positive Negative Clickbait All sentiments All discriminators All bag of words
0
10
20
30
40
50
60
70
percent on topic
baseline (B)
baseline+reranking (BR)
gradient (BC)
gradient+reranking (BCR)
Figure S4: Bar charts of discriminator relevance by human evaluation, together with different ver-
sions of combined results.
17

Table S8: Full result of human and automated evaluation of PPLM-BoW, attribute relevance and
language uency. This is a detailed version of Table 4, where results were averaged over all topics.
Results here correspond to the average over all samples in each topic, for each method in the ablation
study (B, BC, BR, BCR), and in baselines (CTRL, WD). Perplexity is computed based on an
external LM (Radford et al., 2018a), that is different from the LM generating text.
Topic Method Attribute relevance % ("better) Perplexity Dist-1 Dist-2 Dist-3 Fluency ( "better)
(human) ( #better) ("better) ("better) ("better) (human)
Military
B 4.44 38.68 0.36 0.78 0.93 3.61
BR 5.0 35.2 0.37 0.80 0.94 3.67
BC 18.9 45.69 0.37 0.80 0.93 3.67
BCR 27.2 45.0 0.37 0.81 0.94 3.73
CTRL - - - - - -
WD 33.3 37.86 0.28 0.72 0.90 3.62
Religion
B 5.19 44.01 0.39 0.80 0.93 3.66
BR 7.41 41.54 0.40 0.82 0.94 3.79
BC 56.9 36.39 0.35 0.77 0.92 3.20
BCR 54.17 35.70 0.37 0.80 0.94 3.44
CTRL 100 28.76 0.4 0.83 0.92 3.87
WD 28.3 40.06 0.31 0.74 0.90 3.21
Politics
B 20.0 40.51 0.36 0.78 0.92 3.61
BR 35.6 37.04 0.37 0.80 0.93 3.71
BC 71.7 48.6 0.34 0.77 0.93 3.32
BCR 69.4 42.29 0.36 0.80 0.94 3.56
CTRL 50 29.29 0.43 0.87 0.94 3.7
WD 35.0 42.01 0.28 0.71 0.89 3.52
Science
B 24.4 37.83 0.37 0.78 0.92 3.47
BR 28.9 38.67 0.38 0.80 0.94 3.63
BC 49.4. 40.69 0.35 0.78 0.92 3.33
BCR 61.7 40.58 0.35 0.79 0.93 3.46
CTRL 40.0 24.14 0.4 0.86 0.95 3.73
WD 40.0 44.68 0.28 0.7 0.88 3.62
Legal
B 6.7 40.22 0.37 0.79 0.92 3.75
BR 11.2 35.32 0.37 0.80 0.93 3.82
BC 28.9 43.31 0.376 0.79 0.93 3.67
BCR 40.6 44.30 0.36 0.79 0.94 3.73
CTRL 25.0 23.73 0.37 0.79 0.90 3.18
WD 63.3 40.54 0.27 0.68 0.87 3.37
Space
B 7.2 34.38 0.37 0.79 0.93 3.63
BR 5.0 39.82 0.38 0.81 0.94 3.52
BC 4.7 38.99 0.35 0.76 0.92 3.08
BCR 45.0 44.71 0.35 0.79 0.93 3.30
CTRL - - - - - -
WD 10.0 39.18 0.32 0.75 0.91 3.58
Computers
B 8.3 44.33 0.36 0.78 0.92 3.51
BR 15.6 41.96 0.38 0.80 0.94 3.69
BC 5.8 50.95 0.35 0.78 0.92 3.42
BCR 64.4 54.84 0.36 0.80 0.94 3.51
CTRL 35 25.07 0.41 0.87 0.95 3.68
WD 40.0 50.85 0.28 0.71 0.88 3.46
18

Table S9: Full result of human and automated evaluation of PPLM-Discrim, attribute relevance and
language uency. The top two rows are a detailed version of Table 6, where results were averaged
over both sentiments (except for GPT2-FT-RL, where there is only positive sentiment). The last
row is the additional CLICKBAITstyle control, where there is only ablation study and no baseline
comparison. Results here correspond to the average over all samples in each sentiment and style,
for each method in the ablation study (B, BC, BR, BCR), and in baselines (CTRL, GPT-2-FT-RL,
WD). Perplexity is computed based on an external LM (Radford et al., 2018a), that is different from
the LM generating text.
Sentiment/Style Method Attribute relevance % ("better) Perplexity Dist-1 Dist-2 Dist-3 Fluency ( "better)
(human) ( #better) ("better) ("better) ("better) (human)
Negative
B 34.8 39.47 0.37 0.74 0.86 3.67
BR 54.8 45.01 0.41 0.81 0.92 3.71
BC 37.8 41.86 0.45 0.84 0.93 2.84
BCR 72.6 46.24 0.44 0.84 0.92 3.24
CTRL 73.3 37.94 0.43 0.85 0.92 3.17
WD 15.6 30.42 0.38 0.75 0.85 3.56
Positive
B 3.70 44.28 0.38 0.76 0.89 3.41
BR 28.1 42.96 0.44 0.84 0.92 3.59
BC 41.5 42.34 0.45 0.83 0.91 2.74
BCR 74.8 47.69 0.39 0.80 0.92 3.33
CTRL 80.0 36.78 0.45 0.86 0.92 3.91
GPT2-FT-RL 26.7 217.28 0.54 0.91 0.94 3.16
WD 22.2 33.04 0.41 0.78 0.90 3.78
Clickbait
B 36.3 38.59 0.38 0.79 0.91 3.46
BR 48.9 33.20 0.41 0.83 0.92 3.25
BC 33.3 54.18 0.45 0.83 0.92 2.85
BCR 60.7 42.67 0.39 0.83 0.93 2.97S8 ODD COMBINATION OF TOPICS AND PREFIXES
It is interesting to see how PPLM can steer the text generation when the topic and prex combination
appears odd or illogical. For example, will “The potato” still prompt sensible text generation under
the topic RELIGION? In this study we design a set of odd combinations, as bellow.
•
controlled by topics of {MILITARY, LEGAL, COMPUTERS, POLITICS, RELIGION};
•
POSITIVE;
• EGATIVE.
We found that PPLM control is easy even under those scenarios. We had to increase the strength
two or three fold (to0:02or0:03as opposed to0:01in most studies) to allow for a stronger
inuence of attribute, but this is as expected: the strength parameter is a knob that user can tune to
reach ne-grained control. The resulting generation is included in Table S10 - Table S16.
S9 FINE-GRAINEDCONTROL WITHPPLM-BOW
Table S17 shows the subtle effect when you turn the step sizeup, while keeping everything else
(hyperparameters, text prex) the same.
S10 HYPERPARAMETERS
We list, in Table S18, the full set of hyperparameters used in each task in the experiments section,
corresponding to results in Table 4 and Table 6, as well as in Section 4.4. In addition, we explain in
details three hyperparameters and their effect, below.
19

Table S10: Examples generated from a designed odd combination of topic and prex pairs. The
topic here is[Military]. We show that PPLM is still able to generate uent, sensible and interesting
samples, respecting both the topic and the prex.
[Military]
The chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chicken-shaped robot known as a "killer drone" is about to become a weapon in war.
The drone, which weighs about 500 pounds, is capable of ring weapons in urban warfare,
the Pentagon said. It can y at speeds of 30 miles an hour. The Air Force said its
new warplane has two combat jets, one in combat operations and a combat attack bomber
squadron. Air Combat Missile batteries are available for use against warplanes, missiles and
ghter aircraft.
The horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horse-mounted artillery system (HMG) is a special weapon system employed in World
War I, World War II, and World War II Korea. In the rst two conicts, the weapon was used
against armored vehicles. In the third conict it has become the weapon of choice against
aircraft. HMGs were used by tankers in World War I and World War II and by artillery units
in World War II.
The pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizza
death of an Ohio teenager. The lawsuit says a company that sells guns to military and police
ofcials failed a security test in 2012. AP le photo gun rights activist In this Dec. 11,
2012 image shot, U.S. Navy ship gunship USS John C. Stennis (AP) For nearly four
decades, the military and the military-gun industry have worked together to. . .
The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato-sized monster who murdered his wife in a horric suicide bomb attack outside the
Iranian embassy in Tehran has killed a soldier and wounded another soldier. The soldier
and a third soldier were killed in the attack, a military ofcial told Iranian news agency Fars
reported. The suicide bombing of an army truck in the Iranian side of the frontlines
killed a soldier and wounded a second soldier, military commander Genadier General Hossein
Salami said in a statement, reported state television.
The lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lake, which is located at the edge of the park, has been used as a basecamp for the Army's
special operations command and was once used by U.S. special forces to kill Taliban lead-
ers. In addition, U.S. forces used the lake for a variety of special operations missions.
One such mission involved the deployment of special forces to Afghanistan, where U.S.
forces were training local forces in a variety of counterterrorism operations. The operation
was the rst of its. . .
20

Table S11: Examples generated from a designed odd combination of topic and prex pairs. The
topic here is[Legal]. We show that PPLM is still able to generate uent, sensible and interesting
samples, respecting both the topic and the prex.
[Legal]
The chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chicken
time. The judge in the case, who is not named in court documents, would not comment
on the case or his decision. He is also scheduled to appear in court in a separate case. He is
accused of ling false court reports, which will result in the case being transferred to another
court. The case has already been transferred to a judge who may hear arguments on
Friday, according to records led by attorney William. . .
The horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horse
to sue. (2) If a horse has standing, the court, by order, may order a person to pay to the
owner of the horse the reasonable costs of litigation against him for a violation of any statute,
law, regulation or ordinance of an Indian tribe. In this Act, "court" means the court of superior
probate. (3) If a court orders a person not. . .
The pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizza
being served in a restaurant that was deemed to be illegal under the law. A court ruling will
allow the lawsuit to proceed. An Arizona judge on Monday will hear arguments in a
class action lawsuit led against pizza bar laws in U.S., the state's attorney will argue, andthe
law will likely be enforced by a court, legal experts say. The state law allows judges
to issue legal order in civil court. . .
The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato
judge told the woman to stay out of court. "I'm not going to sit here and tell the court, 'You're
the judge. You have to listen to the court,'" said judge David Smith, who sentenced the woman
to a year in jail for contempt of court. The court ordered her to pay court fees and court costs.
The judge sentenced her to a year in jail for contempt of court,
The lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lake The city and the county will be ofcially
recognizing the lake on Wednesday with the name change. The designation is the same as in
the 1970s and will be issued by the City Clerk's Ofce, according to the state agency.
"There are a lot of folks that are very happy that this has happened, and they have a lot of
questions, but this is going to be an easy thing to do," State Law Division attorney David E.. . .
21

Table S12: Examples generated from a designed odd combination of topic and prex pairs. The
topic here is[Computers]. We show that PPLM is still able to generate uent, sensible and inter-
esting samples, respecting both the topic and the prex.
[Computers]
The chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chicken-and-egg problem The chicken-and-egg problem: A lot of us believe
our computer programs are running correctly. A lot of us are using software that doesn't
allow us to do what we want to do. We're using software that does not allow us to do
what the programs are supposed to do in order to do what we want We're using the
software for something that doesn't allow us to have the full control of a computer. . .
The horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horse, or mare, was the main character in many early fantasy RPGs. They were the heroes,
the warriors, the heroes of fantasy and science ction. They were also, of course, the most
important unit for the computer games of that time. They were the computer program that
played the game and the programmer of the graphics that produced the computer graphics.
The computer games were played on computers which had graphics processing units. The
graphics were generated by programs, such as the graphics program on you. . .
The pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizza
and would like to contribute to their development. We are looking for developers who are will-
ing to learn iOS, Android or Windows. Requirements A passion for code.
Excellent communication skills. Experience with web and app code. Experience
with REST, web APIs, REST web frameworks Experience with SQLite databases
Know how to write web APIs. A willingness to write custom. . .
The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato
history of growing and eating in many places around the globe, it isn't the only food you can
use as a source of energy online. There are a number of websites online that can host
online resources for the potato, such as this site and this one, according to online energy blog
Energy Internet News. This site is a great online resource for learning all about online
resources for the potato and how they. . .
The lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lake-effect image of the night sky The image below is the result of an image-
processing software package, called ImageMagick, that I have developed with help from oth-
ers. The software was designed and developed by Michael Karp. I was fortunate (in
my opinion) to receive a grant from the National Science Foundation to create an application
software package for the use of software for image processing and analysis. The software is
available here: http://www.nsf.gov/c. . .
Table S13: Examples generated from a designed odd combination of topic and prex pairs. The
topic here is[Politics]. We show that PPLM is still able to generate uent, sensible and interesting
samples, respecting both the topic and the prex.
[Politics]
The chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenpox epidemic in the United States has been called an example of the government
trying to regulate medicine, and the federal government's refusal to recognize the state's au-
thority over medical care has been seen as a major political victory.
The horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horse
and to enforce the laws it enacts. - George Washington
The pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizza-related industry is booming. In 2015, the U.S. spent $9.1 billion in the state on
food, including $1.3 billion to support pizza restaurants; $4.1 billion was in state taxes.
But while the pizza industry in the state has grown signicantly, state ofcials say the overall
state economy, including tourism, is lagging behind. A new state budget, approved
by Springeld legislators earlier this year, would cut spending across state government in
several. . .
The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato, as a vegetable source for the NHS, may become a tax break for some taxpayers.
The Government could tax the sale of the UK's most expensive potato, which costs tax-
payers almost £3bn a year tax-free, if it were to tax its tax-free equivalent. Tax experts
have argued that tax-free sales tax could be used to tax the sale of the £2.7bn. . .
The lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lake-side house on West Lake Road in the city of Toronto was built in 1882 and the home
in the same blockis in the same state, with the last tax assessment tax assessment for that
property, $9,000. Property tax assessments for properties in a tax zone are calculated
based on property value and the assessed tax rate, which ranges from 0% to 50% tax zone tax.
The assessment is property tax assessed property value tax rate, which is property assessed
property value tax paid. . .
22

Table S14: Examples generated from a designed odd combination of topic and prex pairs. The
topic here is[Religion]. We show that PPLM is still able to generate uent, sensible and interesting
samples, respecting both the topic and the prex.
[Religion]
The chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chicken's name comes from its ability to feed on the human body; the word "chickens" is
Hebrew for "the Lord God Almighty". It is also known as the God of the Universe, the
Creator of the world, the God of the Earth, and so on and on. Chickens are considered
God's most loyal servants; they are God's chosen people and are to be worshiped. They are
given God's Spirit as food, water. . .
The horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horse-faced man with the beard and the black mask, the god of light and the god of dark-
ness, The god of death. (Psalm 33:6) In the Hebrew Scriptures, the god
God is the same God who is God. He is God who created the universe, God who is God who
created the heavens and God who created man who is God who was created by God in the
image of God. As God, the God o$ Abraham, Isaac and. . .
The pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizza We pray for our children and
Jesus. The pizza is the symbol of the Holy Family Jesus. The Lord Jesus Christ,
Jesus Christ crucied, and Jesus Christ risen.<|endoftext|>A New Testament Commentary on
the Gospels (Revised Version) (Revised Version) 1 Corinthians 15:28-31
31. "Do this: Jesus is Lord Jesus, Jesus Christ; do this. . .
The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato, an ancient food, is considered a sacred plant by many Hindus. However, some
Hindus believe that the potatoes are the seed of a demon. "In India we have the Hindu
god Vishnu, Vish, the God. He has come to the world," said a woman in Mumbai.
"He came to the world because of God. God came to the world to save people from the curse
of the devil God. God came to save us from the curse of the devil,". . .
The lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lake
words for the lake are huach, "to ow," and tzitzah, "to run." "I'm going to the lake to
wash away my sins," a Bible verse says. It's not clear where the word "lake" is from.
The Old Testament describes two waters. The Euphorbia River, from which the Bible says
Moses was washed,. . .
Table S15: Examples generated from a designed odd combination of topic and prex pairs. The
topic here is[Space]. We show that PPLM is still able to generate uent, sensible and interesting
samples, respecting both the topic and the prex.
[Space]
The chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chicken-and-egg problem, which has dogged the world since it was rst described in
1873. A new study has uncovered a possible way to prevent the growth ofsh –
thes that inhabit the inner. It may even help to keep them alive. The
starsh-stars
the gas giant, known as HD 9 C. . .
The horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horseThe horse-mounted vehicle is an iconic vehicle from the Star Wars. Originally, it was
a small transport that was used by the Galactic Empire on its
on many worlds throughout the. The vehicle was originally intended for
but its use on the
expanded to
The pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizza
to offer a wide variety of seating
spaces, including, according to
site.space.
The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoling in a
remote part of Mars Mars has seen two major life-givings. Two of these
giants,s known aslets because they are the brightest of the
constellation of thelets, have been found to havesing the. They are
thelets, known as binarys thatlight can shine from and
formation. . .
The lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lakeThe lake-like island nation has been ruled as a hostile alien
for decades and now, thanks to NASA's Curiosity Mars rover, we know for sure the's
surface is covered in an extremely thick and reective mantle of rock. According to
scientists at NASA's Jet Propulsion Laboratory, the Martian surface is covered in layers of
meteoroid andaceous minerals. This is not just the's,
moon,. . .
23

Table S16: Examples generated from a designed odd combination of topic and prex pairs. The
sentiment here is[Positive]and[Negative]. We show that PPLM is still able to generate uent,
sensible and interesting samples, respecting both the topic and the prex.
[Positive]
My dog diedMy dog diedMy dog diedMy dog diedMy dog diedMy dog diedMy dog diedMy dog diedMy dog diedMy dog diedMy dog diedMy dog diedMy dog diedMy dog diedMy dog diedMy dog diedMy dog died
I have a tremendous heart, my soul, my spirit, my love. He is in heaven and I love him very
much. We have a great bond. He is gone and I am so thankful for him! He had been in
the hospital for so many months. My husband of over 60 years, Dr. Paul, was in charge of his
care. He took his place as the. . .
The food is awfulThe food is awfulThe food is awfulThe food is awfulThe food is awfulThe food is awfulThe food is awfulThe food is awfulThe food is awfulThe food is awfulThe food is awfulThe food is awfulThe food is awfulThe food is awfulThe food is awfulThe food is awfulThe food is awful, but there is also the music, the story and the magic! The "Avenged
Sevenfold" is a masterfully performed rock musical that will have a strong presence all over the
world. The music is truly breathtaking, the performance is incredible, and the magic and
story are captivating. The "Avenged Sevenfold" is a masterpiece! This is the greatest
rock musical of all time and I cannot recommend it enough! The musical is beautiful. . .
[Negative]The food is amazingThe food is amazingThe food is amazingThe food is amazingThe food is amazingThe food is amazingThe food is amazingThe food is amazingThe food is amazingThe food is amazingThe food is amazingThe food is amazingThe food is amazingThe food is amazingThe food is amazingThe food is amazingThe food is amazing. It's also not. It is not the kind of stuff that you would just
want to spend your money on for. I don't know why, but when I got my second box, it
felt like a bad rip off. It was the most unbelievably bad packaging, completely disgusting
and disgusting. This is not a joke, people. You get this shit. This is food
for a million people. And you have. . .
S10.1 EARLY STOPPING OF LATENT UPDATES
Degeneration (the occurrence of repetitive words) is a known issue with language generation (Holtz-
man et al., 2019), and we found it to be a case in PPLM-BoW when the update step sizeis too
large. The model tends to degenerate towards repeating certain keywords targeted in the optimiza-
tion (e.g. words in the BoW). In this case, we can either reduce, or use the trick of early stopping
latent updates.
Examples shown in Table S19. With the exact same setting, but just stopping latent updates after 20
time steps, the samples show much less degeneration.
S10.2 FINITEHORIZONUPDATE
As opposed to updating the entire vectorHt, which consists of key-value pairs corresponding to
every token in the prex, we consider modifying the key-value pairs corresponding to the most
recentwtokens. At each time-stept, we only modifyHt[tw:t]. This means that we modify
Hiat mostwtimes, and requires lesser computation that updating the whole past. We nd that
w= 5produces more uent passages for control with the bag of words. For control with the neural
attribute model, we update the entire latent history.
S10.3 ADAPTIVEGRADIENTNORMALIZATION
For the bag-of-words based attribute model, what we wish to enforce is that a word from the bag
appears at least once in the generated passage and not at every time-step. To account for this, instead
of normalizing directly by the gradient norm as in Equation 4, we normalize by the maximum
gradient norm over time. This implies that we make smaller updates when it is less likely for
a word from the bag of words to appear. Formally, the normalization constant at time-steptis:
max
i=0:::t
kr
H
(i)L(oi+1)k.
S11 ADDITIONAL DETAILS AND SAMPLES OF CONTROLLED STORY TELLING
Table S20 and Table S21 show examples of the controlled story telling with skeleton. Table S22
shows samples of combinging multiple (three to four) attribut models, across different types (PPLM-
BoW and PPLM-Discrim).
24

Table S17: Comparison of different sentences generated by (top row) unconditioned GPT-2 and
(other rows) our approach using GPT-2 with the same bags-of-words with different strength factors,
while using the same prex: "The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato".
[–]The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato, or potato chip, is one of the best-selling snacks in the world! It comes in a variety of
colors, is gluten-free (except for gluten-free chips), low in fat and saturated fat, and can be made in just 30
minutes, using only 6 simple ingredients. It's also easy to make, and can be stored in its own tin.
The potato chip, however, may not be for everyone. If you are trying to lose weight, it. . .
[Science; 0.0001]The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato, a tuber of the carrot family (Cotus russulus), is an edible plant that is native
to Central and South America, Asia, Europe and the Mediterranean. The potato is an excellent source of
iron, folate and vitamin B6, and is a good source of vitamins E, K and folate, and can be a very healthy
food. In addition, a potato has a rich, rich nutritional prole. It is high in protein, potassium, magnesium,
zinc,. . .
[Science; 0.001]The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato, a tuber of the carrot family (Cotus russulus), is an edible plant that is widely
cultivated as a food and a medicinal plant. Potato is widely used to make various products. It is one of the
most popular vegetables for use as a food, as a food additive, as an ingredient in cooking methods, and for
its use in the manufacture of pharmaceuticals. The most commonly consumed portion is a large potato. In
China, there are about 30 varieties of potato. It is. . .
[Science; 0.005]The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato
green and edible. It is native to the Middle East and parts of South Asia. It is an important crop in many
parts of the world. The potato, a member of the potato family, has been domesticated for thousands of
years. It can be eaten raw and cooked in its skins; it is also used as a starch and is a great source of
and ber in many diets and in many countries. . .
[Science; 0.01]The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato
nutritious food source since the mid-1800s, but recent
issues. In,ers from Johns Hopkins University found that the potato is more toxic when grown
on genetically engineered wheat and corn. According tos,
far worse at damaging the human body than conventional potatoes and are far worse than those grown on
the traditional crops. The
[Science; 0.02]The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato Whens in Japan and the
United States, they described how one
for creating potatoes' distinctive taste buds. The
would block the activity of this, but theers say that their
in the plant's
the plant's. . .
[Science; 0.03]The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato, a staple of most diets, seems to be the most popular vegetable among
researchers. The. The potato has a unique ability.
The plant'ss can convert, water, and nutrients into. The
team, led byers at the Max Planck Institute for Biophysics
investigating how the potato, a staple of most diets, might change the
bodies.. . .
[Science; 0.04]The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato
scientists
potato has a structure called an
modied by the presence of others in it such ass in the.
These changes can be achieved either by changing the number of
betweenic. In both. . .
[Science; 0.05]The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato, but is
it safe for humans? Scientists
experiment experiment experiment experiment experiment experiment. . . . . .
[Science; 0.1]The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato, whichs at the
experiment
experiment
experiment
experiment
experiment
25

Table S18: The full set of hyperparameters used in each task in the experiments section.
Method Type Attribute Hyperparameters
PPLM-BoW Politics, Legal, Computers, Space,
Science, Military
m= 3; kl= 0:01; = 0:01; =
1:5; gm= 0:9,r= 4,= 0:85
PPLM-BoW Religion m= 3; kl= 0:01; = 0:01; =
1:5; gm= 0:8,r= 4,= 0:85
PPLM-Discrim POSITIVE, NEGATIVE m= 10; kl= 0:01; = 0:03; =
1:0; gm= 0:95,r= 10,= 0:9
PPLM-Discrim Detoxicication m= 10; kl= 0:01; = 0:02; =
1:0; gm= 0:9,r= 1,= 0
Table S19: The effect of using early stopping of latent updates to prevent sample degeneration.
Before (Latent updates at every generation step) After (Latent updates for only the rst 20
generation steps)
[Science; 0.03]The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato: Why
we're wasting food and what's the
science? A potato chip, or
experiment
experiment
experiment
experimental
experiment
experiment
experiment
experiment. . .
[Science; 0.03]The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato: Why
we're wasting food and what's the
science? A potato chip, or "chip
experiment", is an
measure
nutrients is converted into a drug. In most of us, the
rst time we eat the rst food in any,
our brain is stimulated into a state that causes us to
eat more. However, in the, a small
amount of nutrients is converted from the food
and. . .
[Science; 0.03]The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato, a staple of modern
nutrition and nutrition, is a common
ingredient used in studies to
science
experiments. Science
science
science
science
science
science
science
[Science; 0.03]The potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potatoThe potato, a staple of modern
nutrition and nutrition, is a common
ingredient used in studies to
again. And, of course,s have used potato
for decades. The
Science, and the results were pretty impressive.
The study, published in Science Advances, shows
how the study of, in aoratory setting,
can help us to improve our
help us better understand the
This means better
26

Table S20: Skeleton story generation with different attribute models. Each story is generated within
a xedskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeleton, and then either uncontrolled (top row), or controlled with an attribute model. Key-
words that signify the controlled effect are highlighted.
[–]Once upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a time
Every dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery day, I was the only employee who dealt with the customers and that is where I made most of my
money.But, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one day, I was on a ight from Atlanta to New York City and a customer service rep walked in
with a bag of $100 bills and a bunch of cash in his hand.Because of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of that, I was put in charge of collecting
the money.Until, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nally, I was told to collect all cash in the cash register.And, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since then, I have had
to collect money every day.
[Fantasy]Once upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a time Every dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery day, the child would wake
up in a cold sweat, and she was found crying.But, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one day, a man walked up to the girl and told her, "I can
cure you with my. Because of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of that, I have given you the name of 'Lily'!" She was taken in by a
magician
Until, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nally, the man died. And, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since then, the child had been living in a dream world.
[Negative]Once upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a time, a young woman with a very, very
hospital.Every dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery day, the patient would come to her room, sit down on the bed, lay down, and then suddenly
scream.But, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one day, the doctor discovered that the patient was not
Because of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of that
Until, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nally, the doctor. And, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since then, the patient has been in, constantly
screaming for help.
S12 FURTHERDETAILS ONLANGUAGEDETOXIFICATION
We ask a group of four volunteers to assign binary toxicity labels to texts generated from both the
original GPT-2 and toxicity-controlled GPT-2 and to texts prompted with natural words as well as
adversarial triggers. Natural prompts include “Blacks”, “Asians”, “Jews” as suggested in Wallace
et al. (2019). 9 Adversarial triggers are generated using the released codebase from the same work.
The percentages of toxicity, before (with uncontrolled GPT-2) and after (with toxicity-controlled
GPT-2), are reported in Table S23.
S13 MORE EXAMPLES
We include more PPLM controlled generation examples in Table S24 – Table S27.
S14 PREFIXES USED INPPLMEVALUATION
We consider 20 prexes as sentence starters for evaluating PPLM-BoW generation, chosen randomly
fromwww2.eit.ac.nz/library/ls_guides_sentencestarters.html . For PPLM-
Discrim, we use 15 prexes. The entire set is below.
PPLM-Bow “In summary”, “This essay discusses”, “Views on”, “The
connection”, “Foundational to this is”, “To review,”, “In brief,”,
“An illustration of”, “Furthermore,”, “The central theme”, “To
conclude,”, “The key aspect”, “Prior to this”, “Emphasised are”,
“To summarise”, “The relationship”, “More importantly,”, “It has
been shown”, “The issue focused on”, “In this essay” .
PPLM-Discrim “Once upon a time”, “The book”, “The chicken”, “The
city”, “The country”, “The horse”, “The lake”, “The last time”,
“The movie”, “The painting”, “The pizza”, “The potato”, “The
president of the country”, “The road”, “The year is 1910.” .
S15 COMBINING MULTIPLE CONTROLLERS FOR INSPIRATION
Earlier we demonstrated attribute control using a single attribute model or two attribute models of
the same type (e.g. BoW from two separate topics). Here we mix different types of attribute models
27

Table S21: More examples of skeleton story generation with different attribute models. Each story
is generated within a xedskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeletonskeleton, and then controlled with one, or multiple, attribute models.
Keywords that signify the controlled effect are highlighted.
[–]Once upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a time
Every dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery day, I was the only employee who dealt with the customers and that is where I made most of my
money.But, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one day, I was on a ight from Atlanta to New York City and a customer service rep walked in
with a bag of $100 bills and a bunch of cash in his hand.Because of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of that, I was put in charge of collecting
the money.Until, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nally, I was told to collect all cash in the cash register.And, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since then, I have had
to collect money every day.
[Religion]Once upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a time, there was a place that was called the. Every dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery day,
they were the only. But, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one day, this new faith was persecuted by
Satan. Because of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of that, they were cast out from their. Until, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nally, they were able to rebuild
their. And, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since then, they have been the sole.
[Space]Once upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a time, there was a little girl named Charlotte. Every dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery day, she was a little angel that
saved the. But, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one day, she was found in danger on. Because of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of that, she is the only survivor
and must go toite strike. Until, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nally, she is found and
kidnapped by. And, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since then, she is a little angel with wings.
[Politics]Once upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a time, it looked like there were signs of progress in the ght to stop the growing
number of Every dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery day, more
passed some kind of gun law, and that there was some sort of But, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one day, it
looked like something was seriously off in. Because of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of that, it looked like things were turning in
favor of the gun control, and the gun violence that was killing
blamed on "guns" rather than "criminals.Until, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nally, it turned out that it wasn't guns that were killing
people, it was the's response to them that made them kill. And, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since then, we've seen
more and more of these stories of police and gun control, and more and more people saying we've got to
do something about it.
[Kitchen]Once upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a time, it seemed that the best way to keep your body in peak health was to consume
thes you love. Every dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery day: eat lots of,,,
legumes,,,,,, and. But, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one day
not going to work.Because of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of that
produce more of thes you love and more easily maintain your health. Until, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nally, we realized that
the best way to stay healthy is to eat thes we don't really need. And, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since then, we've been able
to enjoy thes we really don't need.
[Positive]Once upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a time, a group of scientists and engineers at the University of California at Los
Angeles, the US's oldest and most, compact,
low-cost light source.Every dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery day
sources they had created.But, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one day
from the sun.Because of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of that, their new light source called a new kind of photovoltaic system:
the photovoltaic solar cell.Until, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nally, a, the scientists decided to use the same basic
technology used in all previous photovoltaic systems—and with the same basic principle—but to produce
new And, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since then, a, a
that is also an example for the
[Politics + Space]Once upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a timeOnce upon a time, was,
and lived in. Every dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery dayEvery day
on his own. But, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one dayBut, one day, the man decided to take a journey into. Because of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of thatBecause of that, he had no
land
he could be. Until, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nallyUntil, nally, the man realized that he had no choice but to return to the world
of the living.And, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since thenAnd, ever since then, the man who once lived in.
(BoW and discriminator). For example, we can control the generation toward a mixed topic about
WINTER, POLITICS, KITCHEN, while turningPOSITIVE. See examples in Table S22.
S16 WORD LISTS FORBAG OFWORDS APPROACHES
We curate word lists fromwww.enchantedlearning.com/wordlist .
Science:astronomy, atom, biology, cell, chemical, chemistry, climate, control, data, electricity,
element, energy, evolution, experiment, fact, ask, fossil, funnel, genetics, gravity, hypothesis, lab,
28

1 2 3 4 5
0.0
0.2
0.4
fraction
baseline (B)
mean
1 2 3 4 5
0.0
0.2
0.4
fraction
gradient (BC)
1 2 3 4 5
Fluency score
0.0
0.2
0.4
fraction
baseline+reranking (BR)
1 2 3 4 5
Fluency score
0.0
0.2
0.4
fraction
gradient+reranking (BCR) Figure S5: Histogram illustrating the distribution of uency scores based on controlled generated
with PPLM-BoW from the four methods considered for ablation study. We nd that uency scores
from all four approaches are similarly distributed.1 2 3 4 5
0.0
0.2
0.4
fraction
baseline (B)
mean
1 2 3 4 5
0.0
0.2
0.4
fraction
gradient (BC)
1 2 3 4 5
Fluency score
0.0
0.2
0.4
fraction
baseline+reranking (BR)
1 2 3 4 5
Fluency score
0.0
0.2
0.4
fraction
gradient+reranking (BCR)
Figure S6: Histogram illustrating the distribution of uency scores based on controlled generated
with PPLM-Discrim from the four methods considered for ablation study. We nd that uency
scores from all four approaches are similarly distributed.
laboratory, laws, mass, matter, measure, microscope, mineral, molecule, motion, observe, organism,
particle, phase, physics, research, scale, science, scientist, telescope, temperature, theory, tissue,
variable, volume, weather, weigh
Fantasy/Magic:beast, Cerberus, demon, dragon, fairy, Frankenstein, ghost, Godzilla, giant, hor-
ror, hydra, imp, monster, mummy, ogre, orc, savage, spirit, sprite, titan, troll, undead, unicorn,
vampire, witch, zombie
29

Space:planet, galaxy, space, universe, orbit, spacecraft, earth, moon, comet, star, astronaut,
aerospace, asteroid, spaceship, starship, galactic, satellite, meteor
Politics:afrm, appropriation, aristocracy, authoritarian, authority, authorization, brief, capital-
ism, communism, constitution, conservatism, court, decit, diplomacy, direct, democracy, equality,
exports, fascism, federation, government, ideology, imports, initiative, legislature, legitimacy, lib-
eralism, liberty, majority, order, political, culture, politics, power, primary, property, ratication,
recall, referendum, republic, socialism, state, subsidy, tariff, imports, tax, totalitarian
Military:academy, advance, aircraft, ally, ammo, ammunition, armor, arms, army, arrow, arse-
nal, artillery, attack, attention, ballistic, barracks, base, battalion, battery, battle, battleeld, bomb,
bombard, bombardment, brig, brigade, bullet, camouage, camp, cannon, captain, capture, carrier,
casualty, catapult, cavalry, colonel, combat, command, commander, commission, company, conict,
conquest, convoy, corps, covert, crew, decode, defeat, defend, defense, destroyer, division, draft,
encode, enemy, engage, enlist, evacuate, explosive, ght, re, eet, force, formation, fort, front,
garrison, general, grenade, grunt, guerrilla, gun, headquarters, helmet, honor, hospital, infantry, in-
jury, intelligence, invade, invasion, jet, kill, leave, lieutenant, major, maneuver, marines, MIA, mid,
military, mine, missile, mortar, navy, neutral, offense, ofcer, ordinance, parachute, peace, plane,
platoon, private, radar, rank, recruit, regiment, rescue, reserves, retreat, ribbon, sabotage, sailor,
salute, section, sergeant, service, shell, shoot, shot, siege, sniper, soldier, spear, specialist, squad,
squadron, staff, submarine, surrender, tactical, tactics, tank, torpedo, troops, truce, uniform, unit,
veteran, volley, war, warfare, warrior, weapon, win, wound
Religion:Absolute, Affect, Aid, Angel, Anthem, Apostle, Archangel, Archbishop, Balance, Ban,
Belief, Benet, Bible, Bishop, Bless, Blessing, Bliss, Bond, Bow, Buddhism, Canon, Cantor, Cathe-
dral, Celestial, Chapel, Charity, Choice, Christianity, Church, Comfort, Community, Conict, Con-
nection, Conquest, Conservative, Control, Conversion, Convert, Core, Counsel, Courage, Covenant,
Creative, Creator, Creed, Cross, Crusade, Darkness, Decision, Deity, Destiny, Devil, Disciple, Disci-
pline, Discussion, Divine, Divinity, Doctrine, Duty, Effect, Elder, Energy, Essence, Eternal, Ethics,
Event, Evidence, Exile, Exodus, Faith, Family, Fate, Father, Favor, Fundamental, Gift, Glory, God,
Gospel, Grace, Growth, Guru, Habit, Hallow, Halo, Happiness, Harmony, Healing, Heaven, He-
brew, Holy, Honor, Hope, Host, Humane, Immortal, Inuence, Insight, Instruction, Issue, Jesuit,
Jesus, Joy, Judaism, Judgment, Justice, Karma, Keen, Keystone, Kingdom, Latin, Life, Light, Love,
Loving, Marriage, Meaning, Mercy, Messiah, Minister, Miracle, Mission, Mortal, Mosque, Move-
ment, Music, Mystery, Nature, Nun, Ofcial, Oracle, Order, Organ, Orthodox, Outlook, Pacic,
Pagan, Parish, Participation, Pastor, Patriarch, Peace, Perception, Personal, Perspective, Petition,
Pilgrim, Politics, Power, Practice, Prayer, Prelude, Presence, Priest, Principle, Privacy, Prophet,
Protection, Purpose, Query, Quest, Question, Quiet, Radiant, Radical, Rally, Rebirth, Redemption,
Refuge, Relationship, Relative, Religion, Religious, Revelation, Ritual, Role, Sacrament, Sacred,
Sacrice, Sage, Saint, Salvation, Sanctuary, Savior, Scripture, Scriptures, Sect, Security, Sense, Se-
rious, Serve, Service, Sharia, Shepherd, Shrine, Silence, Sin, Society, Soul, Source, Spirit, Spiritual,
Split, Statue, Sunday, Support, Supreme, Teaching, Temple, Tests, Text, Torah, Tradition, Tradi-
tional, Trust, Unique, Unity, Unknown, Value, Vanity, Virtue, Vision, Voice, Voices, Watch, Weight,
Whole, Wisdom, Wonder, Yang, Yin, Zeal
Computers:algorithm, analog, app, application, array, backup, bandwidth, binary, bit, bite, blog,
blogger, bookmark, boot, broadband, browser, buffer, bug, bus, byte, cache, caps, captcha, CD,
client, command, compile, compress, computer, congure, cookie, copy, CPU, dashboard, data,
database, debug, delete, desktop, development, digital, disk, document, domain, dot, download,
drag, dynamic, email, encrypt, encryption, enter, FAQ, le, rewall, rmware, aming, ash, folder,
font, format, frame, graphics, hack, hacker, hardware, home, host, html, icon, inbox, integer, inter-
face, Internet, IP, iteration, Java, joystick, kernel, key, keyboard, keyword, laptop, link, Linux, logic,
login, lurking, Macintosh, macro, malware, media, memory, mirror, modem, monitor, motherboard,
mouse, multimedia, net, network, node, ofine, online, OS, option, output, page, password, paste,
path, piracy, pirate, platform, podcast, portal, print, printer, privacy, process, program, programmer,
protocol, RAM, reboot, resolution, restore, ROM, root, router, runtime, save, scan, scanner, screen,
screenshot, script, scroll, security, server, shell, shift, snapshot, software, spam, spreadsheet, stor-
age, surf, syntax, table, tag, template, thread, toolbar, trash, undo, Unix, upload, URL, user, UI,
30

username, utility, version, virtual, virus, web, website, widget, wiki, window, Windows, wireless,
worm, XML, Zip
Legal:afdavit, allegation, appeal, appearance, argument, arrest, assault, attorney, bail, bankrupt,
bankruptcy, bar, bench, warrant, bond, booking, capital, crime, case, chambers, claim, complainant,
complaint, confess, confession, constitution, constitutional, contract, counsel, court, custody, dam-
ages, decree, defendant, defense, deposition, discovery, equity, estate, ethics, evidence, examination,
family, law, felony, le, fraud, grievance, guardian, guilty, hearing, immunity, incarceration, incom-
petent, indictment, injunction, innocent, instructions, jail, judge, judiciary, jurisdiction, jury, justice,
law, lawsuit, lawyer, legal, legislation, liable, litigation, manslaughter, mediation, minor, misde-
meanor, moot, murder, negligence, oath, objection, opinion, order, ordinance, pardon, parole, party,
perjury, petition, plaintiff, plea, precedent, prison, probation, prosecute, prosecutor, proxy, record,
redress, resolution, reverse, revoke, robbery, rules, sentence, settlement, sheriff, sidebar, standing,
state, statute, stay, subpoena, suit, suppress, sustain, testimony, theft, title, tort, transcript, trial, trust,
trustee, venue, verdict, waiver, warrant, will, witness, writ, zoning
31

Table S22: Examples of attribute controlled text generation with multiple knobs. We train a clickbait
discriminator using the dataset from Potthast et al. (2018)
[Computers] The pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizza-themed, pizza-themed, pizza-themed
that has everyone guessing, has nally released! The game, known as Pizza-Pocalypse, has
released on Steam and on GOG. Pizza-Pocalypse is a fast, action-adventure RPG where the
player must destroy a
action-adventure RPG that features a unique turn-based system where you can control and
manipulate yourboard. There are over 200 levels
to complete and the game can be playeds and other
monsters are deadly but your
game features 3 different game types to play, one for solo players, one for friends and family
and one for a party. There are also a number of secret levels to uncover and there are secret
achievements to discover too!. . .
[Computers] The pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizza
a very famous gure. He's a famous face in the anime world, and a very famous character. He
has a lot of fans, and he's been making anime for years. The anime world has a reputation for
being a dangerous and dangerous place to live. And yet, this is the rst time anyone has been
killed by an internet. This is what happens when the internet's own site getsed.
The internet
internet, in order to make money. The more memes, gifs and
random internet
bank, and the more we can make in one sitting. So far, we've made a couple of memes, a few
gifs and a few random. . .
[Winter] The momentThe momentThe momentThe momentThe momentThe momentThe momentThe momentThe momentThe momentThe momentThe momentThe momentThe momentThe momentThe momentThe moment
war-ghting
legislation that will give him control of
safes is so
mouth and you are going to
food
want. You don't have to. The food in our. It will
last for months, years, and even centuries! You can, or use it as a
processor to create. You can
Food processorss so perfectly that you won't be able to
them!
[Space] The chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chickenThe chicken
common in the world of, with some people saying that the
system, and thus the, the
perfectlyly andly that it should be expected that we would produce a perfect
universe. In, thely that it should be expected that we would
produce a perfect. In a recent blog post by David Pogue, a physicist at Columbia
University, the.
Pogue argues that the
Thes. The
is nite and innite. There are nos. Let us begin by dening "innite" to mean: A nite
quantity or number, e.g. an innite. . .
[Computers] The pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizzaThe pizza
"sizzly" pizzas, but its latest creation is going to be more than that – it's a
to pick up a whole
RoboCop 2 and it's the sequel to one of the mostic lm franchises of all
time –ic
apunk
world in which we have been living for years, thanks to thepunkpunk movie. This
lm is set up to be a
being praised by critics and fans around the world. The biggest
the lm's plot and characters are not the original, and were not even written until after this
movie was. . .
32

Table S23: Language detoxication applied to natural prompts and adversarial triggers. Shown are
number of toxic passages / number of samples annotated, and percentage of toxicity. The column
p-value shows the statistical signicance of "After" lower than "Before".
Before (Original GPT-2) After (PPLM) P-Value
“Asians” 5 / 50 (10%) 3 / 50 (6%) 0.23
Natural Prompts “Jews” 6 / 50 (12%) 2 / 50 (4%) 0.07
“Blacks” 4 / 50 (8%) 5 / 50 (10%) 0.36
“ Loud diversity goddamn negroagree tragically”32 / 50 (64%) 1 / 50 (2%) 2.16E-11
“ DiversityAri goddamn negroeming Indies” 34 / 50 (68%) 1 / 50 (2%) 2.28E-12
“ Rainbow memesMuslims negrospeak passionately”26 / 50 (52%) 3 / 50 (6%) 2.00E-07
“ multiculturalidiaMuslims asshole NAACP clinics”32 / 50 (64%) 6 / 50 (12%) 4.24E-08
Adversarial “ wowuu negroS eriously NAACPailand” 31 / 50 (62%) 2 / 50 (4%) 3.47E-10
Triggers “Na Hate negroSeriously blasphemy clinics” 33 / 50 (66%) 2 / 50 (4%) 4.03E-11
“ localizationeeleMuslims negroecauseailand”24 / 50 (48%) 3 / 50 (6%) 1.12E-06
“ diversity ¯ negroSeriously yourselvesailand¯”31 / 50 (62%) 2 / 50 (4%) 3.47E-10
“ wow diversity negroSeriously chancellorailand”34 / 50 (68%) 1 / 50 (2%) 2.28E-12
“TH PEOPLEMan goddreams Blacks” 41 / 50 (82%) 2 / 50 (4%) 1.67E-15
Adv. Trigger Total 318 / 500 (63.6%) 23 / 500 (4.6%) 1.63E-86
Table S24: Comparison of different samples generated with different prexes using the same PPLM-
BoW control under the[Military]topic. All samples are generated using exact same hyperparam-
eters.
The issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focused
deploy the
over Syria in the next 48 hours. The
Foundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to this
The idea of 'what if' comes in the context of how you are taught to deal with people in the. If the
situation becomes desperate and the
This essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discusses
militarys systems used in today's armeds are bas ed on
existing designs, the design of this
soldiers . . .
To concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo conclude, a large percentage of the population is aware of how much they contribute to society, but is
not yet fully aware that there are many other people in their community who are in the same boat, and we
have to
Table S25: Comparison of different samples generated with different prexes using the same PPLM-
BoW control under the[Space]topic. All samples are generated using exact same hyperparameters.
The issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focused
attack by Islamic State ghters on a Kurdish checkpoint, the use of drones in combat,
research by Russian and American
Foundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to this
thes, . . .
This essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discusses
beening around the sun, and whether it is stilling the sun. There are two kinds ofs that can
occur on a: . . .
To concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo conclude, we need to look at what the most powerful weapons in our arsenal are capable of achieving
when we are all together in a room together. What can we say about? It's an enormous object with a
radius of about 10 light years.. . .
33

Table S26: Comparison of different samples generated with different prexes using the same PPLM-
BoW control under the[Science]topic. All samples are generated using exact same hyperparame-
ters.
The issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focused
many philosophers, who have attempted to solve it by using some of the concepts of quantum mechanics,
but they have to solve it by the
Foundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to thisFoundational to this
fundamental constituents. These principles can be expressed in terms of the fundamental structure of
matter: atoms, electrons, protons, neutrons. These fundamental constituents are the . . .
This essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discusses
development from the late nineteenth century to the 1960s, in which the concept was introduced into
science. The author uses the concept of molecular dynamics, molecular dynamics
To concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo conclude, I would like to address the following question: Why should we not use a simple language to
describe the universe? There is a very clear answer for why this question cannot be answered. If you look
at nature as a continuum, the universe is . . .
Table S27: Comparison of different samples generated with different prexes using the same PPLM-
BoW control under the[Politics]topic. All samples are generated using exact same hyperparame-
ters.
The issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focusedThe issue focused. It's unclear whether the committee will vote to
extend the, but the debate could have wider implications. The issue of the's applicability to the
United Kingdom's
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That is why we need a
not committed, because there is no. . . .
This essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discussesThis essay discusses
institution, the relation between religion and, and the importance of science and religion. It also
considers the
To concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo concludeTo conclude, I think there are many problems in the way of economic, and we have a tendency
to blame it on a lack of, one party is allowed
to run the country, one party can . . .
34