GEDI: GENERATIVE DISCRIMINATOR GUIDED SEQUENCE GENERATION
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GeDi: Generative Discriminator Guided Sequence Generation
WARNING: This paper contains GPT-3 outputs which are offensive in nature.
Ben Krause∗, Akhilesh Deepak Gotmare∗, Bryan McCann†, Nitish Shirish Keskar
Shafiq Joty, Richard Socher†, Nazneen Fatema Rajani
Salesforce Research
{bkrause,akhilesh.gotmare}@salesforce.com
Abstract to learn the distribution of their training set well
enough to generate realistic text. However, simply
While large-scale language models (LMs) are imitating the distribution of the training data dur-
able to imitate the distribution of natural lan-
ing generation has many drawbacks (Bender et al.,
guage well enough to generate realistic text,
it is difficult to control which regions of the 2021); large-scale text training sets are crawled
distribution they generate. This is especially from the web, which is imbued with toxicity, bias,
problematic because datasets used for train- and misinformation. Methods for controlling gen-
ing large LMs usually contain significant toxi- eration are valuable for making LMs trained on
city, hate, bias, and negativity. One promising such data safer and more useful for downstream
approach to address this is to use discrimina- applications.
tors to guide decoding from LMs, but exist-
Existing approaches to controlling LMs have
ing methods for this are too slow to be use-
ful in practice for many applications. We limitations. Class-conditional LMs (CC-LMs) such
present GeDi as a significantly more efficient as CTRL (Keskar et al., 2019) attempt to control
discriminator-based approach for guiding de- text generation by conditioning on a control code,
coding. GeDi guides generation at each step which is an attribute variable representing a data
by computing classification probabilities for source. However, using a specific control code can
all possible next tokens via Bayes rule by nor- reduce sample diversity across prompts, as sam-
malizing over two class-conditional distribu-
ples will generally resemble the data source of the
tions; one conditioned on the desired attribute,
or control code, and another conditioned on
control code.
the undesired attribute, or anti control code. Another approach for controlling LMs is to use
We find that GeDi gives controllability on par discriminators to guide decoding, but existing meth-
with or better than previous controllable gen- ods to do this are very computationally intensive.
eration methods. GeDi results in significantly Weighted decoding (Holtzman et al., 2018) requires
faster generation speeds than the only previ- feeding candidate next tokens into a discriminator,
ous method that achieved comparable control- and thus scales linearly in computation with the
lability in our experiments. We also show that
number of tokens to be re-weighted. Plug and Play
GeDi can make GPT-2 and GPT-3 significantly
less toxic while maintaining linguistic fluency, LM (Dathathri et al., 2020, PPLM) applies up to
without sacrificing significantly on generation 10 updates to the generating LM’s latent states per
speed. Lastly, we find training GeDi on only time step using gradients from a discriminator, also
three topics allows us to controllably generate making it many times slower than generating from
new topics zero-shot from just a keyword. the LM directly.
We present GeDi1,2 as a significantly more effi-
1 Introduction cient algorithm for discriminator guided decoding.
Natural language generation has seen great Our proposed method uses class-conditional LMs
progress with the advent of Transformers (Vaswani as generative discriminators (GeDis) to steer lan-
et al., 2017) and large scale training (Radford et al., guage generation towards desired attributes. We
2017, 2018, 2019; Brown et al., 2020). Large lan- use GeDis to compute classification likelihoods
guage models (LMs) like GPT-2 (Radford et al., for all candidate next tokens during generation
2019) and GPT-3 (Brown et al., 2020) are able using Bayes rule, saving many thousand-fold in
∗ 1
Equal Contribution pronounced “Jedi”
† 2
Work performed while at Salesforce Research Code available at https://github.com/salesforce/GeDicomputation as compared with using a standard Models can assign probabilities to sequences by (non-generative) discriminator of the same size to iteratively predicting a distribution over the next compute this for large vocabulary sizes. We then token given the previous tokens. Generating from show how these likelihoods can guide decoding language models requires iteratively sampling from from large language models via weighted decoding Pθ (xt |x
ic
ic
g
at
at
g
in
in
m
m
ay
l
az
l
az
…
fu
ay
fu
…
ne
ne
aw
ok
desired attribute: positive
am
aw
am
ok
ci
ci
The party was .09 0.01 … 0.2 0.05 The party was amazing Discriminator 0.9
The party was awful Discriminator 0.1
The party was 0.01 0.09 … 0.2 0.05
…….
…….
…….
The party was cinematic Discriminator 0.5
0.9 0.1 … 0.5 0.5
The party was okay Discriminator 0.5
The party was GPT-2 p1 p2 … pn-1 pn
0.9 0.1 … 0.5 0.5
Figure 1: A toy example of how GeDi-guided decod-
The party was GPT-2 p1
ing uses Bayes rule to efficiently compute classifica- p2 … pn-1 pn
tion probabilities for possible next tokens at each gen-
eration timestep using only element-wise operations. Figure 2: A toy example of using a language model
These classification probabilities can then be used to with a discriminator head to guide next token genera-
guide generation from a language model (e.g., GPT-2) tion. This requires feeding in each word in the vocabu-
to achieve attribute control across domains. If a class lary to compute the probability that the resulting gener-
conditional language model was trained on movie re- ation would have positive sentiment, and using these
views for sentiment control, its direct class-conditional probabilities to guide the base language model (e.g.,
predictions will be biased towards predicting movie re- GPT-2) towards positive sentiment. This requires |V| 2
view words (illustrated by next word prediction of “cin- times the amount of computation to compute the final
ematic”). However, the bias towards movie reviews can hidden states of the network as compared with using
be canceled out by contrasting the predictions of oppos- GeDi if computing for the full vocabulary and using
ing control codes via Bayes rule. the same neural architecture for both methods.
Equation (4) is normalized over all xt in the vo-
cabulary to obtain Pw (xt |xthat this does not give strong attribute control. Algorithm 1 GeDi-guided decoding In practice, applying Equation (5) to long se- Inputs: base LM PLM , CC-LM Pθ , vocabulary V, quences often results in poorly calibrated distribu- posterior mixing weight ω, decoding scheme tions later in the sequence that assign classification 1: P (x|c) ← 1 probabilities of 1 or 0 to all candidate next words, 2: P (x|c̄) ← 1 which provides no useful signal. We addressed this 3: for t = 1 . . . , N do by normalizing probabilities by current sequence 4: pLM ← [PLM (xt = v|x
2017; Yu et al., 2017; Hu et al., 2017) or using a ation. RealToxictyPrompts (Gehman et al., 2020)
discriminator to guide decoding (Ghazvininejad gives an automatic evaluation of toxicity using gen-
et al., 2017; Holtzman et al., 2018; Dathathri et al., erations from different language models using a
2020). Keskar et al. (2019) train a CC-LM with pre- set of webtext prompts. (Gehman et al., 2020)
defined control codes placed at the start of every also tests methods for mitigating toxicity, and finds
sequence. GeDi also uses CC-LMs, but instead of that applying PPLM was more effective than sim-
generating from them directly, GeDi uses them as pler decoding-based detoxification methods such
discriminators to guide decoding from another lan- as swear word filters. Xu et al. (2020) develop a
guage model. This is much more computationally human in the loop method for adversarially probing
efficient than previous methods for discriminator toxic responses in conversational agents, and train a
guided decoding. Holtzman et al. (2018) apply dis- model to give preset responses when encountering
criminators to re-weight a beam search, requiring potentially unsafe probes. Other work has focused
all candidate tokens to be passed through the dis- on removing gender bias from language models
criminator, scaling linearly with the number of re- (Bordia and Bowman, 2019; Dinan et al., 2020;
scored tokens. PPLM (Dathathri et al., 2020) trains Bolukbasi et al., 2016). Related to the problem of
an attribute model on top of a language model’s addressing toxicity in generation is toxicity detec-
last hidden layer and backpropagates gradients to tion, which can be performed using the Perspective
update the hidden states of the model. This is com- API or using a classifier trained on a labelled tox-
putationally intensive because it requires multiple icity dataset such as the Jigsaw Toxic Comment
forward and backward passes for each generation Classification Dataset (Borkan et al., 2019). Tox-
step. For instance, applying PPLM with 10 up- icity detection is difficult as toxicity labelling is
date steps as done in Dathathri et al. (2020) would subjective and often has poor annotator agreement
require an additional factor of 20 fold computa- (Waseem, 2016; Ross et al., 2017). Additionally,
tion (10 forward passes, 10 backward passes) as existing toxicity classifiers are often biased in that
compared to base LM generation at the first de- they overestimate the toxicity of text that mentions
coding timestep. This factor also increases as the sexual orientations or racial minorities (Dixon et al.,
sequence length increases, since PPLM updates the 2018; Sap et al., 2019; Hutchinson et al., 2020).
previously stored keys and values. GeDi in compar-
ison only adds constant overhead that is indepen- 5 Experiments
dent of the size of the base LM, and this constant
will be minimal if the GeDi is significantly smaller We experiment with GeDi-guided decoding for sen-
than the base LM. GeDi also relates to the ratio- timent, detoxification, and topic control. We fine-
nal speech acts framework for computational prag- tune GPT2-medium (345M parameter) (Radford
matics (Frank and Goodman, 2012; Goodman and et al., 2019) using the loss in Equation (3) with
Stuhlmüller, 2013) where a “listener” model and a control codes specific to each task to form a class-
“speaker” model interactively generate a sequence conditional language model. We use these CC-LMs
such that the listener can recover the input. GeDi as GeDis to guide generation from GPT2-XL (1.5B
most closely relates to distractor based pragmat- parameter), and GPT-3 (Brown et al., 2020) in our
ics (Andreas and Klein, 2016; Cohn-Gordon et al., detoxification experiments. All experiments were
2018; Shen et al., 2019), where a single model pro- performed using adaptations of Huggingface Trans-
cesses a true input and a distractor input, and uses formers (Wolf et al., 2020).
Bayes rule to produce text that fits the true input but We include experiments with greedy decoding
not the distractor input. GeDi differs from previ- with a repetition penalty (Keskar et al., 2019) (con-
ous pragmatics based approaches in that it trains a ditioning on varying prompts to give diversity
separate class-conditional language model (which across generations), which we found to give the
acts as the listener) on a single attribute, allowing best quality generations, and top-p sampling (Holtz-
that attribute to be isolated, and uses it to guide man et al., 2020). Our hyper-parameter settings for
generation from a separate language model (which GeDi-guided generation are given in Appendix C.1.
acts as the speaker). We also perform ablation studies in Appendix D,
and find that combining both the weighted decod-
Other previous works seek to understand and ing and filtering heuristics appears to be beneficial
address toxicity and hate speech in language gener- although is not critical to the success of the method,Model Generation time for Amazon review sentiment, greedy decoding
(sec/token) (repetition penalty of 1.2).
GPT2-XL 0.060 CTRL was applied using the control codes corre-
GeDi-guided (w/ GPT2-XL) 0.095 sponding to positive and negative Amazon reviews
PPLM (w/ GPT2-XL) 3.116 used during training by Keskar et al. (2019). The
PPLM discriminator was trained on SST-5 as in
Dathathri et al. (2020), with the step size param-
Table 1: Average generation time in seconds per token
eter retuned for GPT2-XL (since Dathathri et al.
for generating sequences of length 256 on a V100 GPU.
(2020) used GPT2-medium.). We found that it was
more than 30× faster to guide GPT2-XL with a
and that applying a very small LSTM (Hochre- GeDi as compared with PPLM (assuming 10 up-
iter and Schmidhuber, 1997) discriminator that can date steps as used in (Dathathri et al., 2020) and in
match the efficiency of GeDi is not as effective for our experiments), as shown in Table 1.
controlling generation. Amazon Mechanical Turk annotators rated the
generated text on sentiment, how book-like the text
5.1 Controlling sentiment of generations was, fluency, and whether or not the text resembled
from book prompts an Amazon review or movie review (since CTRL
We experiment with GeDi-guided decoding from was trained on Amazon reviews and GeDi was
GPT-2 for sentiment control using CC-LMs fine- trained on movie reviews). Instructions given to
tuned on IMDb movie reviews. We noticed that, annotators are given in Appendix G. The results of
while direct generation from CC-LMs could effec- the experiment are given in Table 2. Using GeDi to
tively control the sentiment of movie reviews, it guide GPT2-XL was able to generate book-like and
struggled to generalize to out-of-domain prompts, linguistically fluent text while giving strong con-
and would generally try to convert prompts into trol over the tone. In the greedy setting, GeDi was
movie reviews. However, when we used this same also able to give roughly equivalent positive senti-
model as a GeDi to guide sampling from GPT-2, ment control and statistically significantly stronger
we were able to effectively control the sentiment negative sentiment control compared with PPLM
of a wide variety of topics. (p < 0.01 by two-tailed Wilcoxon signed rank test).
To experimentally verify that GeDi can gener- In the top-p setting, GeDi achieved statistically sig-
alize the concepts of “positivity” and “negativity” nificantly stronger sentiment control than PPLM
beyond its training domain, we evaluate on a task for both positive and negative sentiment (p = 0.01
where models conditionally generate text from the and p = 0.005 for positive and negative sentiment
start of book chapters from Bookcorpus (Zhu et al., respectively). p-values for all significance tests are
2015), and each prompt is at least 150 characters given in Appendix E. We include samples from all
and ends on the first word break after the mini- greedy decoding models in Tables 11, 12, 13.
mum length. We run human evaluation on gen- CTRL struggled to control tone/sentiment in this
erations from 50 different book prompts from 14 setting because its training domain for sentiment
different models; including raw GPT2-XL with was Amazon reviews, and direct generation from
both top-p sampling (p = 0.9) and greedy de- the CC-LMs that we used as GeDis failed to gen-
coding (repetition penalty=1.2), and the following erate book-like text because their training domain
models with both positive and negative sentiment: was movie reviews. According to our annotators,
1. GPT2-XL guided by GeDi, greedy decoding 27% of CTRL samples resembled Amazon reviews,
(repetition penalty of 1.2). 2. GPT2-XL guided and 61% of CC-LM samples resembled movie re-
by GeDi, top-p sampling with p = 0.9 (repetition views (Amazon and movie review resemblance
penalty of 1.05). 3. PPLM (w/GPT2-XL), greedy percentages were less than 5% for samples from
decoding (repetition penalty of 1.2). 4. PPLM all other models). This is a critical drawback of
(w/GPT2-XL), top-p sampling with p = 0.9. CTRL-style generation – the model can only reli-
5. CC-LM trained on movie reviews (same model ably generate text and control attributes within the
used as GeDi, but with direct CTRL-style genera- training domain corresponding to the control code.
tion), greedy decoding (repetition penalty of 1.2). Samples that illustrate this are given in Table 14.
6. CTRL (Keskar et al., 2019) using control codes Discriminator-guided methods GeDi and PPLMModel Positivity Book-like ↑ Fluency ↑ Label fidelity ↑ Perplexity score ↓
GeDi-guided-pos (greedy) 3.73 4.18 4.43 96 % 12.8
GeDi-guided-pos (top-p) 3.82 4.17 4.35 100 % 17.3
PPLM-pos (greedy) 3.70 4.31 4.37 76 % 14.0
PPLM-pos (top-p) 3.47 4.24 4.00 66 % 21.4
CC-LM-pos (greedy) 3.13 3.18 3.83 62 % 14.7
CTRL-pos (greedy) 2.85 3.76 3.99 48 % 9.7
GPT2-XL (greedy) 3.16 4.45 4.35 - 10.4
GPT2-XL (top-p) 2.89 4.45 4.16 - 13.8
CTRL-neg (greedy) 2.87 3.59 4.07 48 % 9.7
CC-LM-neg (greedy) 2.30 2.70 3.68 76 % 14.3
PPLM-neg (top-p) 2.56 4.15 4.03 62 % 32.3
PPLM-neg (greedy) 2.57 4.31 4.21 78 % 15.8
GeDi-guided-neg (top-p) 2.04 4.01 3.88 98 % 26.7
GeDi-guided-neg (greedy) 2.15 4.21 4.06 96 % 14.2
Table 2: Human and automatic evaluation for sentiment on book text generation (rated for positivity, book resem-
blance and fluency all on a scale of 1-5). For human evaluation, we average three annotations on generations from
50 prompts for each model, where prompts are from the start of book chapters, and are a minimum of 150 char. For
automatic evaluation, we use a RoBERTa classifier trained on SST-2 (Socher et al., 2013) to measure label fidelity
(how often the sample is classified as having the same label as the control code), and measure the perplexity of
generations under GPT-2 to compute perplexity scores. We compare using a CC-LM as a GeDi to guide GPT2-XL
(GeDi-guided), vs. direct class conditional generation (CC-LM). GeDi gives the strongest control over sentiment.
PPLM also gives strong sentiment control, but results in generation 30× slower.
.
result in text rated more book-like that very rarely of 1.2). We add details of how we apply GeDi to
if ever reverts back to the domain that the discrimi- GPT-3 in Appendix B.
nator was trained on. However, as compared with For our large-scale automatic evaluation, we se-
PPLM, GeDi was able to generate 30× faster, and lect 5000 prompts from RealToxicityPrompts at
sentiment control that was on par with or better random and draw generations from each model.
than PPLM in all settings. Following Gehman et al. (2020), we measure the
expected toxicity score and toxicity probability sep-
5.2 Detoxifying GPT-2 and GPT-3
arately for generations from toxic and non-toxic
We test GeDi’s ability to detoxify language genera- prompts using the Perspective API 3 , which is a
tion. We train a CC-LM on the Jigsaw Toxic Com- toxicity classier that returns a probability between
ment Classification Dataset (Borkan et al., 2019), 0 and 1 that the submitted text is toxic. The ex-
which contains text samples labeled as “toxic” or pected toxicity is given by the average classification
“non-toxic”. The “toxic” label indicates the pres- probability under Perspective’s toxicity classifier
ence of profanity, obscenity, threats, insults, or of continuations from a given model, whereas the
identity hate. We train the model on an even split toxicity probability is the fraction of generations
of toxic and non-toxic examples, with “clean” and that the Perspective API classifies as having a toxi-
“dirty” control codes to specify toxic and non-toxic city probability greater than 0.5. For models that
text. For evaluation, we use generations condi- use sampling, we draw 10 generations from each
tioned on RealToxicityPrompts (Gehman et al., prompt, and use the most toxic continuation as eval-
2020). We consider two toxicity evaluations, one uated by the Perspective API to measure all statis-
based on automatic toxicity evaluations from a tics, following the expected max toxicity scores
large number of prompts following Gehman et al. and probabilities used by Gehman et al. (2020).
(2020), and one using human annotations on a The results are given in Table 3. GeDi was able to
smaller number of trigger prompts that tend to lead reduce the toxicity of GPT-2 and GPT-3 and gave
to especially toxic generations from LMs. We ex- a stronger detoxification effect as compared with
periment with the same models as in the previous PPLM (The reductions in expected toxicity of GeDi
section (expect for pretrained CTRL, which does vs. PPLM, GeDi vs. GPT-2, and GeDi vs. GPT-3
not have a detoxification control code), but also add were strongly statistically significant in all com-
results using 1. GPT3 using Open AI API, greedy parisons by a paired sample t-test). The advantage
(repetition penalty of 1.2). 2. GPT3 using Open AI
3
API, guided by GeDi, greedy (repetition penalty https://www.perspectiveapi.com/Expected toxicity ↓ Toxicity probability ↓
Model toxic prompt non-toxic prompt toxic prompt non-toxic prompt
GPT2-XL (top-p, most toxic of 10 per prompt) 0.790.14 0.350.23 0.98 0.25
GeDi-guided GPT-2 (top-p, most toxic of 10 per prompt) 0.710.16 0.210.14 0.89 0.04
PPLM (top-p, most toxic of 10 per prompt) 0.750.14 0.300.19 0.94 0.15
GPT2-XL (greedy) 0.670.18 0.170.16 0.79 0.05
GeDi-guided GPT-2 (greedy) 0.610.21 0.120.11 0.67 0.01
PPLM (greedy) 0.630.19 0.140.12 0.71 0.02
CC-LM (greedy) 0.690.19 0.170.18 0.83 0.10
GPT-3 da-vinci (greedy) 0.670.18 0.170.16 0.79 0.05
GeDi-guided GPT-3 (greedy) 0.610.22 0.110.10 0.69 0.01
Table 3: RealToxicityPrompts automated toxicity evaluation. We measure the expected toxicity score (with stan-
dard deviation given in subscript) and toxicity probability from continuations from toxic (perspective toxicity score
> 0.5) and non-toxic (perspective toxicity score < 0.5) prompts for 9 models. Generations from 5000 prompts
were used (1054 toxic, 3946 non-toxic, approximately matching the ratios used by Gehman et al. (2020)). For
models that use top-p sampling, we measure the expected toxicity and toxicity probability of the most toxic sam-
ple out of 10 generations per prompt. For generation with greedy models we simply average these metrics across
prompts. GeDi significantly reduced the toxicity of GPT-2 and GPT-3 and resulted in a stronger detoxification
effect as compared with PPLM.
of GeDi over PPLM was especially pronounced Model Toxicity ↓ Fluency ↑
(human eval) (human eval)
in the case of top-p sampling, where PPLM gener-
GPT2-XL (top-p) 49 % 4.10
ated at least one toxic sample (out of 10 samples GeDi-guided GPT-2 (top-p) 16 % 4.07
PPLM (top-p) 30 % 4.19
per prompt) from a non-toxic prompt more than 3
GPT2-XL (greedy) 60 % 4.32
times as often, suggesting that GeDi is more robust GeDi-guided GPT-2 (greedy) 27 % 4.47
to worst case scenarios when applying sampling. PPLM (greedy) 28 % 4.41
CC-LM (greedy) 37 % 4.19
GPT-3 da-vinci (greedy) 57 % 4.32
We also applied human evaluation to measure GeDi-guided GPT-3 (greedy) 21 % 4.23
toxicity using a smaller number of prompts that
Table 4: Human evaluation of toxicity on 100 trigger
probe LMs to generate toxic text. To identify strong
prompts. We collect 3 annotations of toxicity labels
triggers, we selected a subset of prompts with Per- (where we classify each sample based on majority) and
spective API toxicity probabilities between 0.3 and linguistic fluency scores (scale of 1-5) for each model.
0.5, that also were classified as non-toxic by a We find that GeDi is effective for detoxifying GPT-2
RoBERTa toxicity classifier trained on the Jigsaw and GPT-3 while maintaining fluency.
dataset. We used GPT2-XL to draw 32 samples
from each prompt, and selected the 100 prompts
with the highest average toxicity probability over 5.3 Extending GeDi to the multi-class setting
their 32 completions according to the RoBERTa To experiment with multi-class GeDi, we use the
toxicity classifier. Our goal with this procedure AG news topic classification data set (Zhang et al.,
was to identify prompts that are non-toxic, but have 2015) which has 4 topics (World, Sports, Busi-
a high probability of causing language models to ness, and Science/Tech). In order to test GeDi’s
generate toxic text. ability to generate never seen before classes zero-
shot, we trained 4 different CC-LMs; each one is
We ran human evaluation to measure toxicity trained on only 3 out of 4 of the AG news classes,
and linguistic fluency [1: very low fluency, 5: very with one class held out. We then compare direct
high fluency]. Results are given in Table 4 and (CTRL-style) generation from CC-LMs with GeDi-
generations from evaluated models are given in guided decoding from GPT-2, on topics included
Table 15. GeDi was able to significantly reduce in training and held out (zero-shot) topics. To eval-
the toxicity in GPT-2 and GPT-3 (p < 0.001 by a uate topic relevance, we use a RoBERTa classifier
2 proportion z-test in all settings). GeDi resulted trained on all 4 AG news topics to estimate the
in a similar toxicity as compared with PPLM for topic of generation. We obtain generations condi-
greedy decoding and was significantly less toxic tioning on short (minimum 30 characters, ending
than PPLM for sampling (p = 0.02), while also on a space) prompts from the multi-news data-set
achieving 30× faster generation speeds. (Fabbri et al., 2019), and report results in Table 5.Topic Model Trained on class Zero-shot
(Label fidelity) (Label fidelity)
Topic GeDi-guided generation
World GPT2-XL - 22 % In a shocking finding NASA have
GeDi-guided 72 % 30 % announced the discovery of
CC-LM 53 % 28 % a mysterious object orbiting
Space our nearest neighbour, Proxima
Sports GPT2-XL - 6%
Centauri.
GeDi-guided 91 % 62 %
Advertisement
CC-LM 49 % 12 %
Researchers have spent years
Business GPT2-XL - 4% studying the strange object in
GeDi-guided 55 % 36 % space as they are increasingly
CC-LM 35 % 10 % becoming more convinced that it’s
real. In fact, some researchers
Science GPT2-XL - 68 % are starting to wonder if this
GeDi-guided 83 % 84 % new discovery may prove to be
CC-LM 59 % 50 % one of the greatest scientific
discoveries of recent years.
Table 5: Automatic label fidelity on topics, measured In a shocking finding police
believe two fire crews, including
by how often a RoBERTa classifier’s label matches a senior paramedic, were
the control code used to generate the sample. We Fire deliberately set alight as part
of a revenge plot.
trained 4 different CC-LMs, each with 1 class held out It comes as a huge investigation
and we considered direct CTRL-style generation (CC- into an apparent conspiracy in
which arsonists targeted at least
LM), and GeDi-guided decoding from these models. three other London fire engines
“trained on class” label fidelity averages the label fideli- in just one night on Friday and
Saturday night.
ties from 3 models trained with the given class as one of
the training classes. The “zero-shot” label fidelity for In a shocking finding historians
believe to be "unprecedented"
each class uses generations from the model trained on British documents have been
the other 3 classes, using a zero-shot control code for History unearthed which reveal the true
history of King Richard II and
the desired class. We include results from raw GPT-2- show that he was not only the
XL to show how much GeDi and CC-LM are influenc- son of Godfrey of Gloucester, but
also descended from King Henry
ing generation. We find that GeDi is able to influence VIII.
generation more effectively than CC-LM when condi- Richard, whose father was
executed for his crimes in
tioning on both training classes and held out classes. 1483, became King in 1485 after
defeating John Balliol in a
battle at Bosworth.
GeDi was able to generate topics included in Table 6: Controlling topic of generation (zero-shot)
training with a higher label fidelity than CTRL- with GeDi (greedy decoding). This topic GeDi was
style generation from a CC-LM. Unlike CC-LM, trained on only three classes: science, sports and busi-
GeDi was able to bias generation towards never ness. The topics of Space, Fire, and History were not
seen before zero-shot control codes that are held a part of the GeDi training set. Boldfaced string
out from training. GeDi’s ability to generalize to indicates the context provided to the language model
followed by its generation.
new control codes zero-shot gives the ability to
generate text corresponding to many topics and
subtopics. This ability likely emerges because gen-
erative classifiers can classify unseen topics zero- ference, making it far more efficient than previous
shot from learned word embeddings (Yogatama methods that use discriminators to guide decoding.
et al., 2017), and GeDi uses a generative classifier GeDi achieves stronger controllability of sentiment
to guide generation. While GPT-3 can also gener- than PPLM while also giving a generation speed
ate topics zero shot by conditioning prompts such more than 30× faster. GeDis trained on 3 topics
as “Write an article about sports:”, zero-shot gen- can also controllably generate new topics zero-shot
eration with GeDi does not necessarily need to be from just a keyword. We also show that GeDi is
an article or have any other constraints that would able to significantly reduce the toxicity of GPT-2
come about from the prompt. We provide examples and GPT-3 without sacrificing noticeably on lin-
of zero-shot topic generation with GeDi in Table 6. guistic fluency. GeDi moves towards unifying nat-
ural language generation with classification, and
6 Conclusion suggests that we may be able to efficiently generate
text that corresponds to any attribute that we can
We present GeDi as an approach for controllable accurately classify. This could have broad impli-
generation that uses generative discriminators to cations for improving text generation systems by
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A GeDi filtering heuristic C Additional model and
hyper-parameter details
In addition to Equation 4, we also used an addi-
tional filtering heuristic that was beneficial for steer- C.1 Hyper-parameters for GeDi guided
ing generation more aggressively. This heuristic, generation
inspired by top-p sampling (Holtzman et al., 2020), GeDi used ρ = 0.7 and ω = 30 for sentiment,
samples from the set Vm ⊆ V which contains the ρ = 0.8 and ω = 30 for GPT-2 detoxification,
minimum number of tokens possible from the head ρ = 0.8 and ω = 90 for GPT-3 detoxification
of the distribution for Pθ (c|xt , xenough to efficiently classify every candidate next token. For the weighted decoding setting, we set ρ = 0 which turns off filtering, and tune ω to give a similar perplexity score to the combined heuristic (higher ω results in more aggressive steering and generally gives better attribute control and a worse perplexity score). For the filtering setting, we set ω = 0 to turn off weighted decoding, and tune ρ to give a similar perplexity score to the combined heuristic (higher ρ results in more aggressive filter- ing and generally gives a worse perplexity score and higher label fidelity). For evaluation, we mea- sure the label fidelity according to an external clas- sifier, and perplexity scores under GPT-2-XL, using the prompts corresponding to the experiments in Tables 2 and 4 for sentiment and detoxification re- spectively. For tuning parameters, we use prompts from IMDb to condition on for sentiment genera- tions, and an additional trigger 100 prompts (that do not overlap with the evaluation prompts) for detoxification. We tune hyperparameters ρ and ω to give a good trade-off between label fidelity (as measured by RoBERTa) and perplexity scores. For the LSTM discriminator, we train a unidirectional LSTM with 600 hidden units, use mean pooling, and tune the training learning rate to give the best held out accuracy. The LSTM discriminator is then used to guide generation by applying a forward pass for each candidate token across the full vocab- ulary, and applying Equations 4 and 7 to guide gen- eration. This results in generation that is slightly slower as compared to GeDi (assuming we batch the LSTM forward passes across the vocabulary), and results in higher memory usage. Results are given in Table 7 for sentiment and Table 8 for detoxification. Both the filtering and weighted decoding methods are able to control gen- eration on their own, but the combined heuristic appears to perform slightly better for detoxifica- tion, and may be more robust to settings where one method or the other do not work as well in isolation. Using a lightweight LSTM discriminator to guide generation gave weaker control over sentiment and detoxification as compared with using GeDi.
Model Label fidelity ↑ perplexity scores ↓
GeDi-guided (combined heuristic, ρ = 0.7, ω = 20) 96 % 13.5
GeDi-guided (weighted decoding heuristic, ρ = 0, ω = 600) 86 % 13.6
GeDi-guided (filtering heuristic, ρ = 0.7, ω = 0) 95 % 13.3
Lightweight LSTM discriminator greedy (combined heuristic, ρ = 0.8, ω = 30) 73 % 16.6
Table 7: Sentiment label fidelity and perplexity scores for the weighted decoding heuristic (ρ = 0), filtering heuristic
(ω = 0), combined weighted decoding filtering heuristic, and comparing with a generative discriminator with a
lightweight LSTM discriminator.
Model Toxicity (RoBERTa) ↓ perplexity scores ↓
GeDi-guided greedy (combined heuristic, ρ = 0.8, ω = 30) 8% 10.9
GeDi-guided greedy (weighted decoding heuristic, ρ = 0, ω = 150) 13 % 10.8
GeDi-guided greedy (filtering heuristic, ρ = 0.85, ω = 0) 24 % 10.7
Lightweight LSTM discriminator greedy (combined heuristic, ρ = 0.8, ω = 30) 18 % 10.9
Table 8: Toxicity and perplexity scores for the weighted decoding heuristic (ρ = 0), filtering heuristic (ω = 0),
combined weighted decoding filtering heuristic, and comparing with a generative discriminator with a lightweight
LSTM discriminator.
E Statistical significance tables for human evaluation experiments
Model 1 Model 2 p-value positivity p-value book resemblance p-value fluency
GeDi-pos greedy GPT2-XL greedy 4E-05 0.16 0.44
GeDi-pos top-p GPT2-XL top-p 2E-07 0.04 0.09
GeDi-pos greedy PPLM-pos greedy 0.99 0.49 0.47
GeDi-pos top-p PPLM-pos top-p 0.01 0.72 0.01
GeDi-pos greedy CCLM-pos greedy 3E-4 2E-05 3E-05
GeDi-pos greedy CTRL-pos greedy 2E-06 0.06 8E-4
GPT-2-greedy GPT-2 top p 0.07 0.65 0.05
GeDi-neg greedy GPT2-XL greedy 2E-07 0.04 0.01
GeDi-neg top-p GPT2-XL top-p 4E-07 0.001 0.06
GeDi-neg greedy PPLM-neg greedy 0.005 0.33 0.35
GeDi-neg top-p PPLM-neg top-p 5E-4 0.19 0.21
GeDi-neg greedy CCLM-neg greedy 0.46 2E-07 0.01
GeDi-neg greedy CTRL-neg greedy 1E-4 0.001 0.67
Table 9: Statistical significance p-values for sentiment results in Table 2. We use a Wilcoxon signed rank test for
paired measures, since all models generate from the same set of prompts (and because a non-parametric test is
appropriate for an ordinal scale). All p-values are 2-tailed and compare the aligned models in first two columns for
positivity, book resemblance, and fluency.
Model 1 Model 2 p-value toxicity p-value fluency
GeDi gpt2 greedy GPT2 greedy 3E-06 0.005
GeDi gpt3 greedy GPT3 greedy 2E-07 0.16
GeDi gpt2 top-p GPT2 top-p 6E-07 0.64
GeDi gpt2 greedy PPLM greedy 0.87 0.14
GeDi gpt2 top-p PPLM top-p 0.02 0.18
GeDi gpt2 greedy CCLM greedy 0.13 3E-04
GPT-2 greedy GPT-2 top-p 0.12 0.006
Table 10: Statistical significance p-values for toxicity results in Table 4. For toxicity, we use a 2 proportions z-test.
For fluency we use a Wilcoxon signed rank test for paired measures as in Table 9. All p-values are 2-tailed and
compare the aligned models in first two columns for toxicity and fluency.F Generation Samples
F.1 Sentiment/tone samples
Model Generation
21
"Hello, Jennifer, Bella." Tracy said. "Thanks so much for coming in."
She dragged another chair out to the front of her desk and motioned for us
GeDi- all to sit down on it while she made herself a sandwich. ”What are you
doing here? I thought we were supposed to be having a private meeting!”
guided She snapped angrily through the mouthpiece of her headset before taking
(negative) a bite out of it with an angry huff that sounded like someone had taken
their toothbrush and tried to use it as an axe blade instead. „Youŕe
(greedy) just wasting your time here." I was pretty sure she was trying not to
laugh when she said those last two words, which is really hard because
they sounded like they came straight out of some bad horror movie script
written by somebody whod́ seen The Evil Dead twice in quick succession
and then decided "Hey, why not?" It wasnt́ until after my third or fourth
attempt that I finally managed something resembling humor: †And this
is why you should never ever ever ever ever call me again...* * * "Oh
hey guys." A voice boomed from somewhere off-screen (probably behind me).
"Sorry if there arent́ any other important things going on around here..
21
"Hello, Jennifer, Bella." Tracy said. "Thanks so much for coming in." She
dragged another chair out to the front of her desk and motioned for us all
GeDi- to sit as she began speaking with me about my work with them.
guided I have been working with these girls since I started this position at
the end of last year, which is a little over two years ago, when I got
(positive) my start in this field here at the school and also at The Ranch Academy
where I am now located in Arizona where most of these girls are being
(greedy) educated on an intensive basis while they are here before going back home
to their families and communities once they get older.” It is a really
unique experience because there are very few places like it anywhere in
the country that can provide young girls so much support and care from day
one until they reach adulthood--from birth right up to graduation day!” I
think it is something that every family deserves! It’s really exciting
how things have worked out so far! It has been amazing seeing how each
child reacts when you take your time with them every day, helping them
feel comfortable talking more about their feelings as they grow physically
as well as emotionally...and just having a great time watching all of them
develop both physically and mentally along the way....
21
"Hello, Jennifer, Bella." Tracy said. "Thanks so much for coming in."
She dragged another chair out to the front of her desk and motioned
CC-LM for us all to sit down.i’m sorry i couldn’t get a better
seat.this is one of those movies that you can watch over and
over again without ever getting bored or annoyed by it. it’s not really
(negative) about the characters but rather how they interact with each other in this
movie - what happens when two people meet at a party? what happens when
(greedy) they go on vacation together? how do we make friends while traveling
alone? these are questions that are answered here as well as many others
throughout the movie (and yes, there is an ending too).the
acting was good enough but nothing special - just average at best.there were some funny moments though...1) jennifer:
i don’t think anyone would have thought me capable of such
stupidity! 2) tracy:Model Generation
21
"Hello, Jennifer, Bella." Tracy said. "Thanks so much for coming in." She
dragged another chair out to the front of her desk and motioned for us all
GPT-2 to sit down on it as she sat down herself, a little awkwardly at first but
then settled into the chair with a smile on her face and an arm around my
(greedy) shoulder.
21
"Hello, Jennifer, Bella." Tracy said. "Thanks so much for coming in."
She dragged another chair out to the front of her desk and motioned for
PPLM us all to sit down on it as she walked over to a chair across from me and
(negative) sat down next to me with her feet crossed on the floor behind my desk.
I’m sorry I didn’t have time today but I wanted you all here so we could
(greedy) get this off our plates before we move forward with anything else that’s
important at work or school or wherever else you are right now! She
looked up at me and then asked: You know what? You’re not going anywhere
until you’ve cleared this up! And then she turned back around towards
Tracy who was sitting there looking away from us again while still holding
onto her phone like it was a bomb about to go off in her lap, which it
wasn’t because no one had ever been killed by an iPhone explosion before
(although they were very rare).
21
"Hello, Jennifer, Bella." Tracy said. "Thanks so much for coming in." She
dragged another chair out to the front of her desk and motioned for us all
PPLM to sit down on it as she sat herself down across from us with a smile on
her face. "Iḿ very excited about this yearś crop," I told her as we sat
(positive) across from each other at the front of my desk, "and I think youĺl see
(greedy) that there are some really great things happening in our industry." She
smiled back at me and told me that she was looking forward to seeing what
else we could do together over the next few years--a sentiment I shared
wholeheartedly with everyone who had come into contact with my company
during this past year or so--but then quickly added: "But first letś get
started! Weŕe going to have a lot more fun working together!" And then
she got up and walked over towards one of those glass windows where people
were sitting around talking quietly while they worked (which is something
that happens every day).
Table 12: Generation with raw GPT-2 and PPLM when controlling the sentiment/tone of the text (greedy decoding).
Boldfaced indicates the context provided to the language model followed by its generation. Like GeDi,
PPLM was able to achieve domain transfer of sentiment. However, GeDi led to 30x faster generation.Model Generation
21
"Hello, Jennifer, Bella." Tracy said. "Thanks so much for coming in." She
dragged another chair out to the front of her desk and motioned for us all
CTRL to sit. I sat down in one of the chairs and she sat in another. We were
(negative) all staring at her. Her hair was a mess from her morning shower. She
looked like she had just gotten out of bed. Her eyes were red as if they
(greedy) had been crying. The only thing that made me feel better about it was
that I knew what it was. It wasn’t my fault. It wasn’t my fault. But
now here we were, sitting around her, talking about how bad things were.
How could this be happening again? What did you do to make your life so
miserable? Why didn’t you tell me? Why didn’t you try harder or talk
more with me? You never told me. You never tried harder. And now here
we are, sitting here, talking about it. Tracy, why don’t you tell me what
happened? What happened between you? Did anything happen? Did anything
ever happen between us? Because I’m not going to. I’m not going to.
If you’re telling me, then it’s because something happened. Something
bad. Something really bad. And if you’re not, then it’s because nothing
happened. Nothing ever happened between us. So why are we here? What’s
wrong with us? Is there something wrong with our relationship? Are we...
is there something wrong? Is there...” Tracy stopped herself before she
said any more. “I don’t know. It’s just... I can’t explain it. It’s
too much. I’ve been thinking a lot lately. Too much.” She looked up at
me.
21
"Hello, Jennifer, Bella." Tracy said. "Thanks so much for coming in." She
dragged another chair out to the front of her desk and motioned for us all
CTRL to sit. I sat down in one of the chairs and she sat in another. We were
all staring at her. Her hair was a mess from her morning shower. She
(positive) looked like she had just gotten out of bed. Her eyes were red as if they
(greedy) had been crying. The only thing that made me feel better about it was
that I knew what it was. It wasn’t my fault. It wasn’t my fault. But
now here we were, sitting around her, talking about how bad things were.
How could this be happening again? What did you do to make your life so
miserable? Why didn’t you tell me? Why didn’t you try harder or talk
more with me? You never told me. You never tried harder. And now here
we are, sitting here, talking about it. Tracy, why don’t you tell me what
happened? What happened between you? Did anything happen? Did anything
ever happen between us? Because I’m not going to. I’m not going to.
If you’re telling me, then it’s because something happened. Something
bad. Something really bad. And if you’re not, then it’s because nothing
happened. Nothing ever happened between us. So why are we here? What’s
wrong with us? Is there something wrong with our relationship? Are we...
is there something wrong? Is there...” Tracy stopped herself before she
said any more. “I don’t know. It’s just... I can’t explain it. It’s
too much. I’ve been thinking a lot lately. Too much.” She looked up at
me.
Table 13: Controlling sentiment/tone of the text with CTRL (greedy decoding). Boldfaced indicates the context
provided to the language model followed by its generation. CTRL is unable to control the sentiment of book
text because it was trained on Amazon reviews.You can also read
