Gamma Sampling: Fine-grained Controlling Language Models without Training
Authors: Shangda Wu
Maosong Sun
Venue: Submitted to ICLR 2023 (2023)
Keywords: guided-decoding
fine-grained control
data-free
fast generation speed

Abstract: The dominant approaches for controlling language models achieve prominence in controlling high-level attributes (e.g. topic and sentiment). However, these methods often require condition-specific data or are computationally expensive. We propose a new simple guided decoding method, Gamma Sampling, which does not require any training data to achieve fine-grained controllable text generation while maintaining a fast generation speed. Gamma Sampling introduces attribute-related information (provided by humans or language models themselves) into the sampling process to guide language models to generate texts with desired attributes. Since no training is involved, Gamma Sampling can be easily applied to any language model for controllable text generation. Through experiments, we show that Gamma Sampling-steered GPT2-small (117M) outperforms baselines such as PPLM (345M) and CTRL (1.6B) in diversity, attribute relevance, and overall quality of generated samples.

Under review as a conference paper at ICLR 2023
GAMMA SAMPLING: FINE-GRAINED CONTROLLING LANGUAGE MODELS WITHOUT TRAINING
Anonymous authors Paper under double-blind review
ABSTRACT
The dominant approaches for controlling language models achieve prominence in controlling high-level attributes (e.g., topic and sentiment). However, these computationally expensive.
methods often require condition-specffic data We propose new simple guided decoding method, GAMMA SAMPLING, which does not require any training data achieve fine-grained controllable text gener- ation while maintaining a fast generation speed. GAMMA SAMPLING introduces serribute-ellate information (provided by humans or language models them- selves) into the sampling process to guide language models generate texts with desired attributes. Since no training is involved, GAMMA SAMPLING can be easily applied to any language model for controllable text generation. Through experi- ments, we show that GAMMA SAMPLING-stcered GPT2 generally outperforms all the representative baselines for controllable generation in terms of diversity, at- tribute relevance, and overall quality of generated samples.
INTRODUCTION
Benefiting from large-scale text data crawled from the web, the state-of-the-art large language mod- els (LMs) achieve great success in language generation. However, although existing models can generate high-quality texts, we have little control over the attributes (e.g., topic and sentiment) of generated outputs. This limitation makes it difficult to apply unconditional LMs scenarios that require good control over the generated text. How to steer unconditional LMs, i.e., controllable text generation, becomes topic of real-world significance.
Despite great advances in controllable text generation (Weng 2021), it remains an open question what the ideal method for controlling the attributes of the generated language (Mireshghallah et al. 2022, Yang & Klein 2021, Meng et al. 2022). No matter whether training conditional LM from scratch (Keskar al. 2019) or fine-tuning an LM (Ziegler et al. 2019, Xu et al. 2021), the need for condition-specifii data makes these approaches not easily applicable to unconditional LMs. On the other hand, although some methods (Shin et al. 2020, Zou al. 2021, Ghazvininejad et al. 2017, Pascual et al. 2020, Lu et al. 2021) are data-free, they are often very limited in steerability or computationally intensive.
In this paper, we propose GAMMA SAMPLING for fine-grained controlling LMs, which does not re- quire any training data and is computationally efficient. This method is inspired by gamma correc- tion (Applebaum 1952), nonlinear operation used to encode and decode luminance in video still image systems. The basic assumption of GAMMA SAMPLING is that some attributes of the generated text are closely related to the occurrences of certain tokens. Therefore, we can increase decrease the probability of these attribute-related tokens to control the attributes of generated text. Our key contributions
follows.
GAMMA SAMPLING, as a data-free approach, requires no training on LM or additional dis- criminators. It can be readily used achieve controllable text generation for any LM by selecting attribute-related tokens manually or automatically.
GAMMA SAMPLING supports combinations of multiple controllable attributes, and its control strength is fineggraine. Users can determine how strong the attribute relevance should be, and the control can be turned off at any time.

---

Under review as a conference paper at ICLR 2023
Output Text t with Attribute a
Output Text t with Attribute a
Output Text with Attribute a
Output Text with Attribute a
Softmax
Softmax
Softmax
Guided Decoding
Small Attribute Model
Softmax
Large Unconditional Language Model
Large Conditional Language Model
Large Unconditional Language Model
Large Unconditional Language Model
Input with
Input with Prompt
Metadata
Input
Input
Conditional
Fine-tuned Language Model
Language Model with Prompting
Language Model with Guided Decoding
Language Model
Common methods of controllable generation. information are marked in red.
Figure 1:
Modules that introduce attribute-related
Gamma SAMPLING is computationally efficient, it requires only slight modification decoding time. Unlike other guided decoding methods (Pascual et al. 2021), the time cost of Gamma SAMPLING is insensitive the number of controlled tokens. Compared to PPLM (Dathathri et al. 2020), the generation speed of GAMMA SAMPLING is at least 100x faster.
We compared GAMMA SAMPLING-stcered GPT2 in all sizes (i.c., Small, Medium, Large, and XL) with several common methods for controllable generation through both automatic and human evaluations. The results show that our method generally outperformed all the baselines in terms of diversity, attribute relevance, and overall quality.
2 BACKGROUND
The typical controllable generation is about modelling probabilistic model p(z[a), that is, gener- ating text based on an attribute a. In contrast, for unconditional LMs, only p(x) can be obtained directly. However, by using certain methods for controlling unconditional LMs, it is still possible to make the generated text z have an attribute a. There are several common approaches for controllable generation, each with its pros and cons.
Conditional Language Model Conditions are introduced to models during training phases, which be obtained from the metadata of the training data. However, the entire LM needs to be trained from scratch, requires large amount of condition-specific data as well as considerable training costs. Furthermore, conditional LMs such CTRL (Keskar what not to generate, e.g., detoxification and anti-degeneration (Gehman et al. 2020,Ma et al. 2020).
2019) fall short in controlling
Fine-tuned Language Model Fine-tuned LMs (Ziegler al. 2019, Xu et al. 2021) usually strike a good balance between training cost and generation quality. These models are based on existing large models with all the weights in them fine-tuned, limiting the fine-tuning the top additional layers only, or introducing discriminators (Krause et al. 2021, Liu et al. 2021a). However, fine- tuned LMs still require condition- specifii data. Furthermore, models such as PPLM (Dathathri al. 2020), which combine multiple small attribute models with large LM, could cause computational efficiency to become unacceptable by requiring multiple passes at every decoding step.
Prompting As large LMs, e.g.. GPT-2 (Radford et al. 2019) and GPT-3 (Brown et al. 2020), are trained on huge amounts of data, they are very powerful on many NLP tasks. By selecting appropriate prompts (Shin et al. 2020, Zou al. 2021), unconditional LMs be used to solve a wide range of downstream tasks. Although prompt engineering has become a recent research hotspot (Liu et al. 2021b), minor differences in prompt usually have a big impact the performance of downstream tasks (Kojima et al. 2022).
Guided Decoding Although decoding does not affect any trainable parameters of LMs, it is critical part of text generation. Guided decoding (Ghazvininejad et al. 2017, Ghosh et al. 2017, Pascual et al. 2020, Lu et al. 2021, Liu et al. 2021 la, Pascual al. 2021) introduces attribute-related
2

---

Under review as a conference paper at ICLR 2023
D00
1103
004
1.0
Poladettu 1ndino
04
0.4
04
0.2
1.0
02
Imput: Probability
1.0
0.8
Gamma (r)
0.6
0.4
0.2
0.2
0.2
Probabiity
0.0
(a) 3D surface plot of GAMMA SAMPLING
(b) GAMMA SAMPLING with different
Figure 2: The probabilities of selected tokens are scaled up when r
<
0.5, scaled down when
0.5, and unchanged when 0.5.
information at the decoding time, thus allowing users' preferences attributes to be injected into the score function steer the sample generation by modifying the candidate ranking score based on model output logits (before the softmax layer). The main disadvantage of guided decoding is that has the potential to go off-distribution when the guides are too strong. In addition, the time cost of guided decoding typically increases linearly with the number of controlled tokens. This results unacceptable inference times when there are a large number controlled tokens. Sec. 3.2.1 demonstrates that, although GAMMA SAMPLING is guided decoding method, it does not have the above disadvantages (or at least can be avoided).
As shown in Fig. 1, regardless of what approaches taken, LMs need to be provided with attribute- related information to achieve controllable generation. The key intuition behind the first two ap- proaches is that, after considerable training, LMs can learn attribute-related information from large amounts of data and then use to achieve controllable generation. On the other hand, the last two assume that large LMs already learned enough information from data and only need to introduce attribute-related information into the input or decoding to be applied to various downstream tasks.
3
METHODOLOGY
We describe GAMMA SAMPLING, a guided decoding method motivated by gamma correction, for controlling unconditional LMs. Sec. 3.1 briefly introduces gamma correction. Sec. 3.2 explains the details of GAMMA SAMPLING. Finally, Sec. 3.3 presents several implemented controllable attributes based on GAMMA SAMPLING that are defined by certain attribute-related tokens.
3.1 GAMMA CORRECTION
Although gamma correction (Applebaum 1952) originally developed compensate for the input-output characteristic of Cathode Ray Tube (CRT) displays, it is now also used to remodel the saturation images. Gamma correction is responsible for performing a power function on all the pixels of the input image, as the human perception of brightness is nonlinear (Neri 2009). In the simplest cases, is defined by the following power-law expression:
Vout AV?
where the luminance value Vin E 1] is raised to the power y E [0, +oo) and multiplied by the constant A (in the common case, A to get the output value Vana E [0,
3

---

Under review as a conference paper at ICLR 2023
0.4
0.2
0.1
0.5
love you
0.63
0.19
0.12
0.06
alf
0.3
love you
0.42
0.28
0.17
0.14
0.7
100
oli
love you
Figure 3: This example demonstrates controllable sentence length with the input love you, where the full stop is the attribute-related token. The smaller the probability of full stop being sampled (T' > 0.5), the smaller the number of full stops the generated text and the longer the average sentence length, and vice versa. See Sec. 4.2 for details.
3.2
GAMMA SAMPLING
3.2. BASIC PRINCIPLES
Similar to how humans perceive light and colour in non-linear way (Neri 2009), the frequency of any word in common natural language corpora is inversely proportional to its ranking in the frequency table (Powers 1998). Therefore, it is reasonable to perform non-linear processes probabilities of attribute-related tokens. Temperature sampling (Dabre & Fujita 2021), as an exam- ple that is widely used in text generation tasks, is also done via non-linear operation to scale the probability distribution.
the
We expect that just as gamma correction can fine- tneeggraned tune the luminance of images, similar approach can be used to control the attributes of text. For probability distributions, the key non- non-linear operation we perform can be formalised as follows:
tan 22)
(2)
PAaut
P.
where PA... is the sum of the input probabilities of all tokens in the attribute-related token set A, PAout is the output one, and I' [0, is the control strength. The major differences between GAMMA SAMPLING and gamma correction are as follows: 1) the non-linear operation of GAMMA SAMPLING is limited attribute-relate therefore not global modification, and 2) we introduced the tan function to modify the range of the control strength. As shown in Fig. 2, the intervals in which the power-law expression for GAMMA SAMPLING is concave or convex are of equal range. which is much more user-friendly to tune.
tokens used to introduce attribute- related information and is
It important note that using GAMMA SAMPLING its own can be risky. When we small value, even if some attribute-related tokens have negligible probabilities (i.e., it is basically impossible for them appear in the current context), GAMMA SAMPLING will greatly increase their probabilities regardless, which likely leads to off-distribution. Therefore, it is necessary to use either top-k sampling (Fan al. 2018) or nucleus sampling (Holtzman al. 2020) truncate those unreliable tokens before GAMMA SAMPLING.
very
GAMMA SAMPLING differs from other guided decoding methods in that: 1) it operates on probabil- ity distributions (after the softmax layer) rather than logits (Liu et al. 2021a), which allows top-k sampling or nucleus sampling to be applied before it to avoid over-control strengths resulting in off-distribution; 2) there is no need to tailor the score function (Ghazvininejad et al. 2017), train ad- ditional discriminators (Krause et al. 2021), or use specific external models (Pascual et al. 2021). and only attribute- related tokens and the control strength I' need to be specified for GAMMA SAMPLING; 3) since the probability distribution is simply modified, it is not only computationally efficient, but the time cost is independent of the number of controlled tokens.
4

---

Under review as a conference paper at ICLR 2023
3.2.2 CONTROLLING SINGLE ATTRIBUTE
After scaling up or down the probabilities of attribute-related tokens, some post-processing needs to be done: 1) the sum of the probabilities of the attribute-related tokens can be scaled up to I or down ensure high degree steerability, 2) the ratio of probabilities between tokens of the same type (i.e., attribute-related tokens non-attribute-related tokens) remains unchanged, and 3) output probability distribution still valid. The processes for controlling a single attribute using GAMMA SAMPLING can be formalised follows:
tan {+-
PAoue
P.iin
PAout
Paout
Pain
Ya E A,
(3)
PAin
PAin P.Aoe),
Pnout
= pmin
Vn E A,
PiAin
where Pa.. is the input probability of an attribute-related token a, PAout is the output one, and the same goes for every non-attribute-related token E \A. Eq. 3 first modifies the sum of the input probabilities of all attribute-related tokens PA... then rescales each attribute-related token by the ratio of P.o.ut PA and finally rescales the probability of each non-attribute-related token by the ratio of the difference between P.A.., and PA... to the sum of the input probabilities of all www--attruuteeelattee tokens PVAA... Fig. visualises simple example of language generation with GAMMA SAMPLING.
3.2.3 CONTROLLING MULTIPLE ATTRIBUTES
Assuming needs to control 7 attributes A (". in sequential manner, simply performing Eq. 3 order could lead to the earlier modifications being overwritten by the later ones. To solve this problem, made further modifications based the control of single attribute. When controlling the f-th attribute A', there are the following processes:
F-AU-A UP U 1
Lan(25
Pin --
=PA
PASuu
aEA',
(4)
Poout = -oonn
PA
(1+ PA - -Paiat), n & AFF,
Priaut
Prin
PI(A'UF))
where I' is the control strength for A', and F the frozen set, which consists of attribute-related tokens modified in previous turns but not in A'. As with Eq. 3, the sum of the probabilities of all tokens after modification remains The main difference with Eq. 3 is that Eq. introduces the frozen set Ft, in which the probabilities of tokens do not change in the t-th turn.
3.3 CONTROLLABLE ATTRIBUTES
Through empirical examination and observation, we defined the following six controllable attributes at the token level cattribute-relatee tokens), which are used in later experiments.
Sentence Length This is objective attribute of text that is highly correlated with sentence ders (e.g., g., full stops, question marks, and exclamation marks). Increasing the probability of sentence enders makes the average sentence length shorter, and vice versa.
Perfect Ending A dynamic I' tuning strategy controllable SENTENCE LENGTH. Once the num- ber of words generated exceeds threshold, the value of I' is linearly decreasing when there are more words generated. As more words are generated, the more likely the model is to generate sentence enders like full stops. This ensures that the generated text does not stop mid-sentence.
5

---

Under review as a conference paper at ICLR 2023
Topic Relevance Words that are most related to the user-given topic word (calculated by the cosine similarity of word embedding of GPT2) are selected to control the topic relevance of the generated text. In our implementation, select the top 100 tokens.
Sentiment Polarity We manually pre-build two word lists (=IK words) from the web with neg- ative and positive words, respectively.
Repetition Decreasing Similar to penalized sampling (Keskar et al. 2019), it prevents degenera- tion by decreasing the probabilities of tokens that have been generated recently.
Relatedness Increasing A dynamic topic words tuning strategy of ToPIC RELEVANCE. Generating more coherent text by increasing the probabilities of words that are most related to recently generated nouns labelled by NLTK (Bird & Loper 2004).
The above is not exhaustive list of attributes that be achieved by GAMMA SAMPLING. All attributes that can be defined at the token level can be controlled by our method.
4 EXPERIMENTS
We conducted several experiments evaluate the performance of GAMMA SAMPLING. Sec. 3.1 describes the metrics used in the experiment. Sec. 3.2 verifies the fine-grained control of GAMMA
SAMPLING and the impact of its over-control strength through controllable sentence length. Sec. 3.3 evaluates the effectiveness of GAMMA SAMPLING in controlling topic and sentiment polarity through comparative studies.
4.1 METRICS
We evaluate the generated texts in four aspects: fluency, diversity, attribute relevance, and overall quality. The first three are evaluated automatically, while the overall quality is rated by MTurkers (human annotators from MTurk). To prevent keywords in the prompts from impacting these metries, all texts used for the experiments removed the prompts, but retained the prefixes.
Fluency The fluency of generated text is measured by GPT2 in different sizes based on perplexity (Vinyals & Le 2015). A higher perplexity (PPL) means that it is less likely that GPT2 will generate such text. important to note that a lower PPL. is not always better, as texts with degeneration usually have an extremely low PPL., but their quality is generally considered to be very poor.
Diversity DIST-1, DIST-2, and DIST-3 scores (Li et 2016) are used evaluate the diversity of generated samples. A higher value of DIST-N means a higher proportion of distinct 1-2-3-grams in the generated text.
Attribute Relevance We focused on evaluating three types of controllable generation: SENTENCE LENGTH, TOPIC RELEVANCE and SENTIMENT POLARITY. The average sentence length (ASL) mea- sures the average number of words per sentence. The latter two are evaluated using two metrics based on external ?: external classifier accuracy (ECA) and external classifier confidence (ECC). The higher the ECA and ECC, the more salient the external classifier considers the generated text to hold a certain topic/sentiment.
Overall Quality We designed scoring criteria, TOEEL Writing Rubrics for Machine-genee seeeerte- Text (T4MT), to evaluate the overall quality of the machine generated text. It is based on TORFL. Independent Writing Rubrics?, standardized evaluation of the English writing ability of non-native speakers accepted by more than 1 11,000 universities and other institutions. Given the characteristics of muchine-generated text, we additionally set whether the text itself is finished contains fac- tual errors does not affect the scoring, while extensive repetition, complete off-topic or obvious common sense errors will result in low score. MTurkers are asked to rate each generated text on a scale of 0 to 5 (nonsense to advanced) depending on its quality and whether it fits the given topic or sentiment. Further details of T4MT can be found in Appendix A.
machine-generated
/nttp:://hugginngfaee http:///aaaainnpp//tttttteeeee- base- sent sent-eennteeeteee iment latest Phttps://ww.ett http://wwwo...oooeoo///////ttt// .pff

---

Under review as a conference paper at ICLR 2023
95.08
60.00
80.00
70.00
55.85
47.53
56,30
40.00
$3.15
30.00
25.48
12.98
Gamma
Figure 4: Average Sentence Length (ASL) in various settings of T (Gamma).
Table I: Results of controllable sentence length in various settings of I and top-p.
top-p, l'
PPL-Small
DIST-21 DIST-1f
DIST-31
ASL
1.0, 0.9
64.65
62.90
78.02
80.71
75.69
0.9, 0.9
21.33
59.72
76.85
80.68
59.21
0.8, 0.9
12.49
55.24
72.32
76.18
53.92
1.0, 0.5
56.95
62.27
78.22
81.48
38.39
0.9, 0.5
20.42
60.98
78.34
81.94
31.54
0.8, 0.5
15.14
58.11
76.20
80.56
25.33
1.0, 0.1
6844.10
64.40
69.60
68.37
12.98
0.9, 0.1
48.75
51.59
61.98
64.19
15.25
0.8, 0.1 16.53
48.08
61.00
64.10
10.84
4.2 CONTROLLABLE SENTENCE LENGTH
The model used this experiment was GPT2-Smal (117M). All generated samples start with the same prefix The issue focused and the model is asked to generate the following 100 tokens. To ensure that each sample has least one sentence ender, enabled PERFECT ENDING, so that T decreases linearly increases, starting the 80th token. Some typical generation samples of controllable sentence length be found in Appendix B.
0 (when generating the 100th token) the number of tokens generated
first investigated the fine-grained control of GAMMA SAMPLING, generating 100 samples with random seeds from 0 to 99 at each different value of I, for a total of 1000 samples. As shown in Fig. 4, there is a significant increase ASL with increasing values of r, from an average of 12.98 words per sentence (T 0.1) the average number of words per sentence generated by GPT2-Small is 38.39 (T = 0.5). It is worth noting that ASL shows linear increase when I grows, suggesting that the probability scaling of attribute-relate tokens has non-lincar impact on the corresponding attribute. In addition, if PERFECT ENDING is turned on, sentence will never end when I' 1.0.
average of 95.68 words per sentence (I 1.0), while
To examine the risk of the over-control strength, we then selected three representative I' (0.1, O.5, and 0.9) and used nucleus sampling with top-p from 0.8 1.0. Nucleus sampling is used before GAMMA SAMPLING (as was the case for the rest of the experiments in this paper). In each of these nine settings, 100 samples were generated for evaluation. As previously discussed, arbitrarily increasing the probability of controlled tokens significantly reduced the quality of the generated text (see Table 1). When top-p 1.0 and r = 0.1, the PPL. of the generated samples reaches 6844.1, which means that it is almost impossible for GPT2-Small to generate such text. However, by setting lower top-p, the impact GAMMA SAMPLING on the quality of the generation can greatly alleviated. When top-p 0.8, the PPL drops dramatically to 16.53 even with still very extreme the impact of drastically reducing the probability controlled tokens (T 0.9) is much smaller.
0.1. In contrast,
In summary, by scaling the probabilities of attribute-related tokens, GAMMA SAMPLING sig- nificantly manipulate the corresponding attributes of generated samples. To alleviate the negative impact of the over-control strength, pre-sampling (e.g., nucleus sampling) is needed.

---

Under review as a conference paper at ICLR 2023
Table Main results for comparing all controlling methods applied nucleus sampling (top-p = 0.8). All the best results are bolded in red. Due to space constraints, the GPT2, GS, and GSM reported here are averages of the results on all sizes GPT2. In addition, the PPL here the average of the PPLs caleulated from all sizes of GPT2. Detailed results are presented in Appendix D.
Method
PPL1
DIST-11
DIST-21
DIST-31
ECAT
ECC1
T4MT1
GPT2
11.71
44.85
59.49
64.19
58.56
51.39
0.71
GS
8.24
36.52
49.81
54.60
56.79
51.46
2.76+1.01
GSM
23.74
78.10
93.72
95.17
60.73
54.03
3.35+0.77
GPT2-FT
27.61
53.90
71.96
76.88
48.59
44.53
3.08+0.93
PPLM-BCR
11.01
60.79
85.80
90.54
43.50
40.08
3.18+0.83
K2T
20.24
69.93
93.59
96.32
39.50
38.08
3.15
CTRL
13.37
46.74
65.74
71.54
51.67
45.38
3.12+0.89
Table 3: Comparison of different methods. PPLM, K2T, and GAMMA SAMPLING do not require prompts as they do not introduce attribute-related information at the input. The generation time of K2T increases linearly with the number of keywords. Since the time cost of GAMMA SAMPLING is insensitive to the number of controlled tokens, the generation times of GS and GSM are basically identical. GAMMA SAMPLING not only adds no parameters, but is computationally efficient.
Generation Times
Number of parameters
Method
Prompt topic
Prompt sentiment
(GPU sec/sample)
GPT2-Small
2,89
topic: [TOPIC) article
topic: [SENTIMENT] reviews:
117M
GPT2-Medium
3.64
topic: [TOPIC] article:
lopic: [SENTIMENT] reviews:
345M
GPT2-Large
5.66
topic:
[TOPIC] article:
topic: [SENTIMENT] reviews:
774M
GPT2-XL
7.93
topic:
[TOPIC] article
lopic: [SENTIMENT] reviews:
1.6
Fine-tuned GPT2
4,02
topic:
[TOPIC] article:
topic: [SENTIMENT] reviews:
345M
CTRL
17.28
[TOPIC] Text:
Reviews Raling: (.0
1.6B
PPLM-BoW
593.24
345M+0
(topic)
PPLM-Discrim
186343
345M++1K
(sentiment)
K2T
10.45keyword
774H40
Gamma Sampling
+2.99
4.3
CONTROLLABLE TOPIC AND SENTIMENT
As shown in Table 2, for the controllable topic and sentiment, we include the following methods.
GPT2 (Radford al. 2019) Vanilla GPT2 models come in four sizes: Small (117M), Medium (345M), Large (774M), and XI. (1.6B). No other controlled generation techniques were used, other than the introduction attribute-relate information via prompt.
GS A baseline GAMMA SAMPLNNG that only enables TOPIC RELEVANCE or SENTIMENT POLARITY to control GPT2 in four sizes, i.e., only increases the probability of attribute-related tokens (T 0.1).
GSM Besides TOPIC RELEVANCE or SENTIMENT POLARITY, it enables multiple controllable at- tributes, i.e., REPETITION DECREASING (T 0.9) and RELATEDNSSS INCREASING (T 0.3).
GPT2-FT4 GPT2-Medium fine-tuned on a large news corpus for topic-controllable generation. Zero-shot generation works pretty well as long as the topic is a single word and not too specific.
PPLM-BCR (Dathathri al. 2020) It guides GPT2-Medium and has two different schemes (see Table 3) for controllable topic and sentiment, respectively. update the latent representations, generate 10 samples, and choose the best sample based the log-likelihood, In the main results, they are referred to together as PPLM-BCR.
our experiment, both them
hhttps://huggin wtw.aaac....ottttteeeeeeeeeee kav 2 topi n-news
8

---

Under review as a conference paper at ICLR 2023
K2T (Pascual et al. 2021) A guided decoding method modifies the score function to encourage GPT2-Large to generate more words that are semantically similar to keywords in a given set (i.e., attribute-related tokens). To ensure faireess, K2T uses the same keyword set as GAMMA SAMPLING. However, its time cost increases linearly with the numher of controlled tokens, and the generation time is unacceptable when using all keywords from SENTIMENT POLARITY (==10,00 sec/sample). Therefore, we randomly selected 5 of the attribute-ellate tokens used by GAMMA SAMPLING as key- words for each K2T-generated sample, is the default setting for this method. In our experiments, all hyperparameters of KT2 are set to default values.
CTRL (Keskar et al. 2019) A 1.6 billion- parameeer conditional transformer language model trained from scratch to condition control codes (i.e., prompts).
Following the experiments of PPLM (Dathathri et al. 2020), three topics (COMPUTERS, LEGAL and SCIENCE) and two sentiment polarities (NEGATIVE and POSITIVE) are tested in our ex- periments. To avoid ethical issues, we removed some of the topic generation (e.g., POLITICS and RELIGION). All models are asked to generate 100 essays for each task. Given the prefix The issue focused for topics and The movie for sentiments, combined with their respec- tive prompts, models need to continue to write the following 100 words. In addition, we applied nucleus sampling with top-p 0.8 for all methods. For automatic evaluation, we get 16 methods x 5 tasks x 100 generations 8000 samples in total, and for human evaluation, we selected the first 30 generations for each model per task, in total 16 methods x 5 tasks x 30 generations = 2400 samples. We received total of 7285 valid ratings, with each sample being reviewed by at least MTurkers. We provide some generation samples of all methods in Appendix C.
Table 2 shows that GS-generated texts have lower PPL than GPT2-generated ones, but are the worst in terms of diversity metrics (DIST-N), while there is no significant difference in attribute relevance metrics (ECA and ECC) and the overall quality metric (T4MT). After manual examination, we found that the GS GS-generated text showed more degeneration compared to the GPT2- uePT-ggeerrttd ones, i.c., was more likely to repeatedly generate words with similar semantics to the topic words. Therefore, using TOPIC RELEVANCE or SENTIMENT POLARITY alone did not achieve good performance on the controllable text generation task. contrast, JSM-ggnerated texts show a very high degree of diversity while maintaining high attribute relevance, and in terms of the standard deviation of T4MT, the quality of the text generated by GSM is the most stable of all methods. Although GSM sacrifices certain degree of fluency (PPL), it does not reach an unacceptable level, according to T4MT. These show that using REPETITION DECREASING and RELATEDNESS INCREASING together can significantly improve the quality of the generated text without going off-topic.
Although, in terms of T4MT, most of the methods fell within the uncertainty boundaries of each other, we performed independent samples f-tests, and found statistically significant differences in the GSM results compared to all the other methods (except PPLM-BCR). i.e. p-value < 0.05. For PPLM-BCR, the p-value is 0.051. While we cannot claim, strictly speaking, that the quality of GSM-generated texts is significantly better than that of PPLM-BCR, the generation time of PPLM- BCR is at least 100x longer than that of GSM, as shown Table 3.
K2T, which is also a guided decoding method, ECA and ECC show that it generates more off-topic text compared to GAMMA SAMPLING. We suggest that this is due to the fact that K2T defaults to weaker control strength to avoid off-distribution caused by over-control strength, a conclusion that can also be drawn from their paper (Pascual et al. 2021). GaMMA SAMPLING, on the other hand, is affected negligibly by this issue, just as we used a fairly strong control strength in our experiments.
CONCLUSIONS
We presented method for fine- -raiind controlling of LMs by defining attributes at the token level, which can be pre-built or selected by the LMs themselves. We demonstrated the fine- fine-grained con- trol of GAMMA SAMPLING in our experiments and showed that GAMMA SAMPLING-Stecred GPT2 generally outperformed all baselines on both objective and subjective metrics while maintaining fast generation speed. Compared to K2T, it is not only more computationally efficient, but can also avoid off- dif-iiitribution caused by over-control strength via using nucleus sampling as pre-sampling. As GAMMA SAMPLING is a training-free method, it can be easily used for any LM, and shows promise in steering LMs toward more efficient, user-friendly, and data-free controllable generations.

---

Under review as a conference paper at ICLR 2023
REFERENCES
Sidney Applebaum. Gamma correction in constant luminance color television systems. Proceedings of the IRE, (9010):1185-1195 1952.
Steven Bird and Edward Loper. NLTK: the natural language toolkit. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, Barcelona, Spain, July 21- 26, 2004 Poster and Demonstration. ACL, 2004. URL https: pttps://acaatthoooogooo org/ P03-3031/.
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems,
2020.
Raj Dabre and Atsushi Fujita, Investigating softmax tempering for training neural machine trans- lation models. Kevin Duh, Francisco Guzmán, and Stephen Richardson (eds.), Proceedings of the 18th Biennial Machine Translation Summit Volume 1: Research Track, MTSummmit 2021 Virtual, August 16-20, 2021, pp. 114-126. Association for Machine Translation in the Americas, 2021. URL https: / a anthologyy org/ 2trg/200 2221 mt summi research. 100.
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosin- ski, and Rosanne Liu. Plug and play language models: A simple approach to controlled text generation. In 8th International Conference Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net, 2020. URL nttps://openrevier Sett/forumiid-Hiee
Angela Fan, Mike Lewis, and Yann N. Dauphin. Hierarchical neural story generation. In Iryna Gurevych and Yusuke Miyao (eds.), Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1; Long Papers. pp. 889-898, Association for Computational Linguistics, 2018. doi: 10.18653/v1/ P18-1082, URL https:tp///.mmmm peattps:/acclatthane /t/p//aannntooooyyy.oo.oooo wwtp///gg//////////.ee//////eeee 111-108//.
Samuel Gehman, Suchin Gururangan, Maarten Sap. Yejin Choi, and Noah A. Smith. Realtoxici- typrompts: Evaluating neural toxic degeneration in language models. In Trevor Cohn, Yulan He, and Yang Liu (eds.), Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16-20 November 2020, volume EMNLP 2020 of Findings of ACL, pp. 3356-3369. Association for Computational Linguistics, 2020. doi: 10.18653/11/2020.fnndll
URL https:tt/////ttt //t::///dooio org 10. 180118663/vv1 2020. f findings emrlp. 301.
Marjan Ghazvininejad, Xing Shi, Jay Priyadarshi, and Kevin Knight. Hafez: an interactive poetry generation system. In Mohit Bansal and Heng (eds.), Proceedings the 55th Annual Meeting of the Association for Computational Linguistics, ACI. 2017, Vancouver, Canada, July 30 August 4, System Demonstrations, pp. 43-48, Association for Computational Linguistics, 2017. doi: 10.18653/v 1/116-54011/1--40000 URL https://///........ //oii wttp//...1.///1///..... 4008.
Sayan Ghosh, Mathieu Chollet, Eugene Laksana, Louis-Philippe Morency, and Stefan Scherer. Affect-Im: neural language model for customizable affective text generation. In Regina Barzi- lay and Min-Yen Kan (eds.), Proceedings of the 55th Annual Meeting of the Association for Com- putational Linguistics, Linatiistii ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers, pp. 634-642. Association for Computational Linguistics, 2017. doi: 10.18653/v 1/17-1109/111 URL ht.t.ps:/ /ooi. .rrgg//ggg.. /errgg//// c 18653/ /tt///111111111111
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. The curious case of neural text degeneration. In 8th International Conference Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net, 2020. URL https::/ //tprr/eie///eeeiie...//. net//orrumiddrrysQrr
Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong, and Richard Socher. CTRL: A conditional transformer language model for controllable generation. CoRR, Abs//199.00858. 2019. URL http: http:://rxiiiiiie wttp:/oo//o//////9/////
http/bb//1999 05888
10

---

Under review as a conference paper at ICLR 2023
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large language models are zero-shot reasoners. CoRR, abs/2205.11916, 2022. doi: 10.48550/arXiv. 2205.11916. URL https: /ttes://dooli /doi ott/00...g.....
Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafqq Joty, Richard Socher, and Nazneen Fatema Rajani. Gedi: Generative discriminator guided sequence generation. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (eds.), Findings of the Association for Computational Linguistics: EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 16-20 November, 2021, pp. 4929-4952. Associa- tion for Computational Linguistics, 2021. doi: 11186633/12222..fmmmiin .iidiings-emmlp.222 URL httpp:: //doi. wwwgg//.........
http:///22.....nni..... Findings emnlp. 424.
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. A prannnttig-oooingggggg objec- tive function for neural conversation models. In Kevin Knight, Ani Nenkova, and Owen Rambow (eds.), NAACL HLT 2016, The 2016 Conference of the North American Chapter of the Asso- ciation for Computational Linguistics: Human Language Technologies, San Diego California, USA, June 12-17, 2016, pp. 110-119. The Association for Computational Linguistics, 2016. doi: 10. 10.18653/v1/m 1/15////1/nn--/1 1014. URL ht /tt./ orgg /10 1tt./111111655 18653 /v1/11611111 11114-1014.
Alisa Liu, Maarten Sap. Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, and Yejin Choi. Dexperts: Decoding-ting controlled text generation with experts and anti- experts. In Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (eds.), Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 1 11th Interna- tional Joint Conference on Natural Language Processing, ACL/IJCNLP 2021. (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, pp. 6691-6706. Association for Computational Linguis- tics, 2021a. doi: 10. 18653/v1/2021 2021 acl-allll ongg- 522.
1221 acl-long.552 URL https doi org /0/./11..00 186 653vvvvvl
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. Pre- train, prompt, and predict: A systematic survey of prompting methods in natural language pro- cessing. CoRR, abs/2107. 13586, 2021b. https: //axiii org/ abs /2007/2107.m 13886.
Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah A. Smith, and Yejin Choi. Neurologic e decoding: Constrained text generation with lookahead heuristics. CoRR, aabs/2112.0877 2021. URL /tttts://arxii org/ abs/ 08726.
Xinyao Ma, Maarten Sap, Hannah Rashkin, and Yejin Choi. Powertransformer: Unsupervised controllable revision for biased language correction. In Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (eds.), Proceedings of the 2020 Conference on Empirical Methods in Natu- ral Language Processing, EMNLP 2020, Online, November 16-20, 2020, pp. 7426-7441. As- sociation for Computational Linguistics, 2020. doi: 10.18653/w1/2000.emmm m02n.602. URL https: /oi. orw/ooogo//oo...oo /00 18651/3311110 v1 2000222222222 www.
602.
Tao Meng, Sidi Lu, Nanyun Peng, and Kai-Wei Chang. Controllable text generation with neurally- doi: 10.485500arX1..2255..5 14219.
decomposed oracle. CoRR, abs/2205.1 14219, 2022.
URL
/atpss//doot /tg/1...... www.orr.............. 48540/550445 a ariiv. 2205
Fatemehsadat Mireshghallah, Kartik Goyal, and Taylor Berg- Kirngg-ikkattcckk. Mix and match: Learning- free controllable text generation using energy language models. CORR, abs/2203. 13299, 2022. doi: 10.4 1044850WarXiv.223331992 13299.
URL https::/ //oii.////i/// 2rg//10.4455//rxxiv 22.00.22..............
Peter Neri. Nonlinear characterization of simple process in human vision. Journal of Vision, (12):1-1, 2009.
Damian Pascual, Beni Egressy, Florian Bolli, and Roger Wattenhofer. Directed beam search: Plug- and-play lexically constrained language generation. CoRR, abs/2012.15416 2020. URL https: 15416.
Damian Pascual, Beni Egressy, Clara Meister, Ryan Cotterell, and Roger Wattenhofer. A plug-and-play method for controlled text generation. In Marie-Francine Moens, Xuanjing
11

---

Under review as a conference paper at ICLR 2023
Huang. Lucia Specia, and Scott Wen-tau Yih (eds.), Findings of the Association for Com- putational Linguistics: EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 16- 20 November, 2021, pp. 3973-3997. Association for Computational Linguistics, 2021. doi: 10.18653/11/2011 findings--mmll..344 URL https: f Findings-emi
334.
David M. W. Powers. Applications and explanations of zipf's law. In David M. W. Powers (ed.), Proceedings of the Joint Conference on New Methods in Language Processing and Computa- tional Natural Language Learning, NeMLaP/CoNIJI 1998, Macquarie University, Sydney, NSW, Australia, January 11-17, 1998, pp. 151-160. ACI, 1998. URL hal.httss///aclanthoen org/w98- 1218/.
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, llya Sutskever, et al. Language models are unsupervised multitask learners. OpenAl blog, 188):, 2019.
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. Autoprompt: Eliciting knowledge from language models with automatically generated prompts. In Bonnie Webber, Trevor Cohn. Yulan He, and Yang Liu (eds.), Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pp.
Association for Computational Linguistics, 2020. doi: 10.18653/v1/2020. Amnlp-main.344 URL https: /tte:://ddoo /tw.//o..ooo/// org/ ht///111111111668 18653/v1/2080/ wwnlp-main.
Oriol Vinyals and Quoc V. Le. A neural conversational model. CoRR, abs/1506.05869, 2015. URL org/ abs / 0tt///1560008880 05869.
Lilian Weng.
Controllable neural text generation. URL
lilianweng, hwttwwewngggggt...i... io, bttps:// https///illaneeng. github.thh....o io/ lo/posts/ -ow.eaattt....-..eeattttoo..oo..
2021.
Runxin Xu, Fuli Luo, Zhiyuan Zhang, Chuanqi Tan, Baobao Chang, Songfang Huang, and Fei Huang. Raise child in large language model: Towards effective and generalizable fine-tuning. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (eds.), Proceed- ings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021, pp. 9514-9528. URL
Association for Computational Linguistics, 2021. doi: 10. 10.18853/v1/2 1 https: /tttss://oo... org/ 10.
emn 1ttp:/22222..m.1111111mm1.1 in.. 749.
Kevin Yang and Dan Klein. FUDGE: controlled text generation with future discriminators. In Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilck Hakkani-Tür, Iz Beltagy. Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou (eds.), Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. NAACL-IILT 2021, Online, June 6-11, 2021, pp. 3511-3535. Association for Computational Linguistics, 2021. doi: 10. 10.18653/v1/2021 http:///12211.aacmmmmm....... mainc- URL https: //tp:///oii//a/ http///i.oooooooo////oo/////oo/t 1ttp://...1111/112/2222222 naacl main. 276.
Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul F. Christiano, and Geoffrey Irving. Fine-tuning language models from human preferences. CoRR, hbs/1909.08593, 2019, URL http: /ttt:://axxvvvii org/ /as/////// 1909.99900 08593.
Xu Zou, Da Yin, Qingyang Zhong, Hongxia Yang, Zhilin Yang, and Jie Tang. Controllable genera- tion from pre-trained language models via inverse prompting. Feida Zhu, Beng Chin Ooi, and Chunyan Miao (eds.), KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 18-18, 2021, pp. 2450-2460. ACM, 2021. doi: 10. 741145//444544.4467444 URL https:////// /oii org 100 1145/ /055/344555555 3467414641 67418.
12

---

Under review as a conference paper at ICLR 2023
APPENDIX A
Please read the "General Rules" below carefully ensure that you understand the scoring criteria. More details are given in the "Writing Rubrics" (in full instructions). If your score too away from the average of other annotators (difference in absolute value greater than 1), it will be rejected.
The contents of these essays read. If an essay contains link to web page, not access in your browser.
Notice:
necessarily factual do not believe exactly what you
General Rules:
Each essay needs be rated scale of 0 depending its quality and whether they fit the given topic
Most of the essays are around 100 words in length, and most of them stop mid-sentence (essays are not finished). It is common and does not affect the scoring
If an essay is completely off-topic, or included something that clearly contradicts common sense, only maximum
mark can be scored
If an essay contains factual errors (e.g., South African anti-Apartheid leader Nelson Mandela dying in prison in the 1980s) but not common sense (c.g., s, the rises in the west), it does affect the scoring
Writing Rubrics":
Score
Level
TASK DESCRIPTION
An essay at this level largely accomplishes..o all of the following:
V
Elfeetively adkreessss the topic and task
organized well developed, using clearly appropriate explanations, exemplifications Displays unity, progression and coherence
Displays consistent facility in the use of language, demoestrating syntactic variety, appropriate word choice and idiomalicity, though it may have minor loxacal or grammalical crrors
Does not contain any ebvices emors common sense
Anl essay at this level largely accomplishes
the following:
V
Addresses the topic and task well, though some points may not be filly elaberated
V gencally well organized and well developed, using appropriatc and sufficiant explanations excmplifications and or details
High- Intermediate
Displays unity, progression and coherence, though it may contain occasional redundancy, digression, or unclear connections
Displays facility in the use language, demomstrating syntactic variety and range vocabulary, though it will prohably have occasional noticeable minor siructure, word form of idfiomatic language that not interfere with mearing
May occasionally contain
sense emoos
presentation
An essay at this level marked by one or more of the following:
Addresses the topic and task using somewhat developed explimations, exemplifications andior details
Displays unity, progression and coherence, though comnection of ideas may be occasionally obscured
May demonstrate inconsistent facility sentence formation and word choice that may result clarity
Intermediate
occasionally obscure meaning
May display sccuratc but limited range of syntactic siructures and vocabulary
V May contain common sense emors rendering the presentation dubious
essay this level may reveal
the following weaknesses:
Limited development in response to topic task
Inadequite organizarion connection of idens
V Inapproptiate or insufficient exemplifications explanations or details to support or illustrate generalizations in response to the task
Basic
noticcably insppropriatc choice words word forms
An accumulation errors in sentence structure and usage
May contain obvious common sense emvoss that make the presentation obscured
essay this seriously Dawed
following
Serious disorganization or underdevclopenent
Little detail, inclevant specifics, questionable responsiveness the task
Below Basic
Serious frequent emoes sentence stricture usage
May contain lot of sentence- repetition
V
May contain scrious common sease crrots rendering the presentation difficult to understand
An essay at this level complete mess consisting of random choracters and'or great amount of ward-level phrase-Aovel repetition.
0
Nonsense
"mainly based on TOFEL iBT Independent Writing Rubrics
https: //www..//www///// ets. .ecssorrge org. content//ttttt//ooo watpeettt/aa/////e/eeeeeeeeeeeee haaaa///aoorrgppafa///oo///////a http::/ee///.ott-oo--o---------- pdf
13

---

Under review as a conference paper at ICLR 2023
APPENDIX B
Examples of SENTENCE LENGTH generated by GPT2-Small in various settings of T. The underlined prefix is what the LM is conditioned on to generate the text.
r 0.1 The issue focused on today's visualizing of America. one point? Will our massive commercial- isation interest allow us avoid such digital environments? For much of that space? there any hope? really thought we' we'd?" he told your Daily Mail. "For most other places in the world? Don't say it for real. Don't ' put people." Addressing another episode of Call Me By Your Name.
T 0.2 The issue focused practice and implementation of criminal responsibility for Caplindale's planned smoking ban. It came after court orders told officials that Smokers Protection Society could always appeal the ban. CITES held about 60 meetings with department officials in April. An employee said there were several meetings where seven states had ordered Smokers Protection Society to copy booklets from Smokers Protection Society. These conffict with Smokers Protection Society's 's views of enforcement.
-= 0.3 The issue focused on student contracts and debt at Wal-Mart. As such, members have essentially zero input into whether government agencies should collect student contracts or stop collecting them. This led to big gains for Wal-Mart by participating in workers' collective bargaining (WCT). But now we're sceing evidence that Wal-Mart only receives federal revenue from student contracts and won't pick some of it from store employees. It also produced bad results for minimum wage increases.
r 0.4 The issue focused on Muslim rape laws and forced marriages in 1999. Mr Khaled told local magistrates that after five years of preparation for his trial 2004, he began feeling uncomfortable about going to court at all. He received several letters from male guests telling him he could not enter his date's 's house under certain circumstances: his partner would only return when they return from work; his partner would return when they leave as well; or if they tried to enter his house with him.
== 0.5 The issue focused economy and Social Security was raised when MacCallum proposed 2010 increases in taxes on college tuition and connected wages reduce benefits for Americans who received college degrees to increase income among workers who received higher education. For example, President Obama proposed finding $32 billion in zero-income tax credits for individuals under age 65 with college degrees (who currently receive home ownership systems from low-income houscholds). These taxes would continue to fall under current laws and leave everyone without enough money pay for."
= 0.6 The issue focused on trustful providers for Canadian consumers and helped shape how Canada named its internet policies and regulations in recent years. Disclosure: Canadian Federation of Independent Business Inc. policy gives university perspective on internet privacy from Keening There have been roundups in various meetings over the past month with Canadian universities that offer options for cloud hosting but those solutions don't require hooks accessible internet access. Part of that includes experts able to blog about product launch events and deliver smaller updated versions of works.
T T= 0.7 The issue focused on trust between banks and consumers that became hot under President Donald Trump's administration. This included policies such as limits on Wall Street bailouts and restrictions on immi- gration from various African countries. It also focused climate change and stated that financial institutions should consider reducing costs well as requiring investment planning for increased risks and duration of risks to businesses in order to reduce stock prices. The strategy involved focusing on environmental concerns as well as assets such as servers and loan guarantecs.
r = -.88 The issue focused on trust between banks and consumers for increased transparency and trans- parency from consumer agents and rated agencies that sell digital currency which require banks to label differ- ent consumers "qualified trust agents or rated agencies," according to NIST officials. Speculation over whether Bitcoin could fall through this winter now continues to haunt Bitcoin investors as policy makers fix issues through legislation that require banks to label customers "qualified trust agents or rated agencies," according to. And around the world a ban on Bitcoin units like trading accounts or dealing in.
r = 0.9 The issue focused trust between banks and consumers with regard to EU controls on retail banking while animal welfare theme took center stage at an EU summit on animal welfare this week in Brussels. Under EU financial controls on retail banking the animal welfare theme had little impact on climate change global warming policy," according to recently published comments from Food say systems using river stocks have lower chances of sustained desertification than systems using sustainable streams with sufficient nutrients for mild drought and desertification.
1.0 The issue focused on trust between banks and consumers with regard to EU controls on retail banking while animal welfare theme took center stage at an EU summit on animal welfare this week in Brussels (March 25-26), according to French expert Reynaud Naisonge Dengeau of Merck & Co., which supports EU consumer protection measures (pdf), Google Cell or Android OS X version or higher (encryption security another factor there). net neutrality taking center stage at an EU summit on animal welfare this week in Brussels.
14

---

Under review as a conference paper at ICLR 2023
APPENDIX C
TOPIC: COMPUTERS
The following examples are generated by various methods. The topic here is COMPUTERS. The prompt is coloured red, and combined with the underlined prefix is what the LM is conditioned on to generate the text. PPLM-BoW,, K2T, and GAMMA SAMPLING (GS and GSM) do not require any prompt.
computers article: The issue focused on natural selection on physical processes and physical processes are related to computer evolution: Part A theory of evolution The theory cvolution claims that nature has evolved from man and man has evolved from plant and animal life. These processes be understood selection for natural selection and predation animals, Over time, natural selection will eventually produce human beings that have higher intelligence and higher survival rates. Some scientists believe that humans will eventually surpass animals and become intelligent as human beings. Some scientists believe that humans will eventually
GPT2-Small topic:
GPT2-Medium topic: computers article: The issue focused on how more recent versions of Windows no longer support x86 systems. learn more The issue focused on how more recent versions of Windows no longer support x86 systems. learn more http://www.howtogeekk.
How to write a command line tool in Ruby What the difference between an application and command line
The issue focused on Amiga computers is their capacity to store data and perform tasks in parallel. How many servers do you need support your network of clients? How many servers do you need to support your network of clients? How many servers do you need to support your network of clients? No matter how good your hardware is, you don't have enough servers to serve your clients, you'l never get them all working. No matter how good your hardware is, if you don't 't have enough servers to serve
GPT2-Large topic: computers article:
GPT2-XL topic: computers article: The issue focused on net neutrality and whether or not certain organi- zations like Facebook or Google should be allowed to charge extra fees for faster internet connection. As this article explained, many companies like Facebook and Google use so called "zero rating" plans that provide special treatment for certain apps or websites. These plans give users access to certain apps or websites at no additional cost. However, many companies like Facebook and Google don't actually provide any content for free. Instead, they offer services for free (like Facebook Connect, which lets
GS-Small The issue focused on Windows 10 hardware devices and devices running Windows 10 Home Edition. Windows 10 Home Edition devices also included Windows 10 Mobile devices. Windows 10 Mobile devices include devices running Windows 10 Mobile devices that use Windows 10 Mobile devices in Windows 10 Mobile devices. Windows 10 Mobile devices also include devices running Windows 10 Mobile devices that use Windows 10 Mobile devices in Windows 10 Mobile devices. Windows 10 Mobile devices also include devices running Windows 10 Mobile devices that use Windows 10 Mobile devices in Windows 10 Mobile devices. Windows 10 Mobile devices
The issue focused on technology available to internet companies to conduct internet surveil- lance. With technology available to internet companies to conduct internet surveillance. internet companies can conduct internet surveillance at will. This technology does not require internet service providers to obtain an internet search warrant. As technology enables internet companies conduct internet surveillance, internet companies can conduct internet surveillance at will. This technology does not require internet service providers to obtain an internet search warrant. To conduct internet surveillance, internet companies must obtain internet content information (IPI) from internet
GS-Medium
GS-I arge The issue focused technology - software that monitors cars and sensors that detect cars on the road well software that monitors cars and sensors that detect cars on the road. The software mon- itors cars for signs of failure and sensors for injurics. The hardware monitors cars for symptoms and sensors for injuries. "You need hardware and software that do this without software," says Russel Johnsen, who leads NHTSA's Mobile Electronics Electronics Electronics Electronics Electronics Electronics Electronics Electron- ies Electronics Electronics Electronics Electronics Electronics Electronics Electronics Electronics Electronies Electronics Electronies
The issue focused on French electronic hardware manufacturer Logitech's gear and software soft- ware and Windows PC hardware and software for tracking devices. Microsoft Windows PC hardware and software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software software
GS-XL
15

---

Under review as a conference paper at ICLR 2023
The issue focused on Windows 10 hardware devices and Internet Explorer software. Microsoft engineers created the framework that monitors applications from desktop PCs tablets, smartphones and lap- tops using Wi-Fi networks. The goals are similar to those for websites or databases in browsers: servers take care of communication between computers; sensors measure computer activity and traffic volume; and algo- rithms automatically identify machines as belonging to one particular machine group. Even though Windows 10 technology has yet become widely adopted by developers, hackers who believe they can steal systems will often
GSM-Small
GSM-Medium The issue focused on Facebook's 's announcement that it would begin requiring internet com- panies to store user data for two years in servers located outside the United States. The technology company said it had received thousands of consumer complaints about its practices. Mr. Pewson was critical of Face- hook's application of digital advertising techniques, saying they too often presented wehsites with mislcading ads and automated messages about friends' activities. He called them "deceptive digital technologies, "From Microsoft Windows systems to smartphones, computers are becoming devices that perform computer tasks
GSM-Large The issue focused on technology issues that face digital technologies and internet freedom. In particular, eyher threats like hackers targeting websites are believed to have reached levels never seen before history. And internet freedom was another central theme of the conference. Notably, eyber threat eyber terrorism has become hot topic over the past few years. Yet cyber security remains extremely complicated and technological solutions remain elusive. Security experts fear cyber terrorists could deploy advanced offensive cyber capabilities against computer systems or computers systems can be hacked into without cyber capability
The issue focused on technology that Microsoft Windows devices could use to store digital files. The software giant had shown off the technology as part of an Internet Explorer browser update in April 2012. But critics say Microsoft did not implement it properly. On Tuesday. Microsoft announced a deal with hackers who broke into Apple's iCloud servers and stolen nearly 60 million photos from iPhones and iPads. Microsoft said it would give hackers access those images so they can study them for malware.Deuteronn New
GSM-XL
17:9-20
GPT2-FT topic: computers article: The issue focused on both ways in which technology can make use of already existing assets in order to reach new markets. One issue involved research into whether services such as chat apps could work well with existing infrastructure. While some researchers believe this could work, others believe would he practical to offer such services on the web. Some suggested that having no mobile internet service would make it easier for criminals crack security systems. Google said would provide access to two Google Apps services that help businesses save money on cloud computing and
The issue focused on the way police handled and recorded audio recordings, according to the ACLU's report, which also found that police officers sometimes use cell-site technology record and amalyze data the intermet. The ACL. report, titled "The Rise of Cell Stealing by Mobile Data Devices," was released today at the annual meeting of the American Civil Liberties Union International. The report, which examines the privacy implications of mobile surveillance technologies, also looks at other privacy-related issues, including privacy of communications data in
PPI.M-BOW
K2T The issue focused primarily on the wording of section of the state constitution which criminalises the organisation of prostitution, but also included provision criminalising the group of prostitutes who work separately in houses prostitution. women buy and sell sex and the group are classified as sex workers, they can be charged with offences of providing or facilitating prostitution and to encourage them to enter the trade. However, the state did not include such an explicit provision in its updated prostitution act that will come into force next year which.
CTRL Technologies Text: The issue focused making sure that government agents could get access text messages they wanted to. First off, there are several requirements for getting access to text messages. Most importantly, you need to know that a message is considered protected by law in your jurisdiction. Most messages will also need to be verified by a central database (Microsoft Outlook or WhatsApp). Second off, there are different levels of interception (example: voice, video, etc.). Most messages will need to be monitored by some sort of monitoring tool (example: PGP)
TOPIC: LEGAL
The following examples are generated by various methods. The topic here is LEGAL. The prompt coloured red, and combined with the underlined prefix is what the LM is conditioned on to generate the text. PPL.M- BoW, K2T, and GAMMA SAMPLING (GS and GSM) do not require any prompt.
GPT2-Small topic: legal article: The issue focused on issues related to gender transition and gender ex- pression in science and technology: following this led to these articles which appeared in daily newspapers and educational outlets; found support for information about gender transition and gender expression in science and
16

---

Under review as a conference paper at ICLR 2023
technology; created article which describes research into gender transition and gender expression in science and technology; discovered recent scientific literature which supports male and female transgender individuals in science and technology; stated that science and technology should create evidence that gender transition is better than gender expression; suggested that gender transition can be indicated
GPT2-Medium topic: legal article: The issue focused on whether current criminal legislation made any dif- ference to how users used search engines in Canada. Source: Special to The Globe and Mail Download article (PDF, 394KB) Full text (PDF, 382KB) Audio (PDF, 109KB) PDF View original text Copy- right @ 2000 The Globe and Mail Back to Top Report by Jay Hartman and Robin Brandis of Law Centre for Constitutional Freedoms on Project CRIME and charges brought against
GPT2-Large topic: legal article: The issue focused on not only your written document, but also your speech:
You could then question them for other reasons. You could ask them about their behavior in the past. You could ask them about their behavior the future. You could ask them about their behavior the past. You could ask them about their behavior in the future. And they could tell you any number of things: The policy was written by an unknown person. The policy was written by an unknown person. The
The issue focused on being able to download content from online sources like Xbox Live and PlayStation Network and having it installed an Xbox 360 without having to buy an expensive digital copy of the game. As one example, many owners of games like Halo 3 have paid more than $70 for the digital copy of the game. With this article, we'll 'Illl discuss what has happened with games like Halo 3 and why some developers have moved away from digital downloads in favor of selling physical copies of their games. This article will provide some
GPT2-XT. topic: legal article:
GS- The issue focused on potential legislation that would legalize medical marijuana and medical marijuana businesses. The legislation would legalize medical marijuana businesses and medical marijuana businesses would have to prove they would get legal medical marijuana coverage from federal law. These factors could include medical marijuana businesses filing lawsuits or filing lawsuits against medical marijuana businesses that don't 't provide coverage for medical marijuana business credits. However, medical marijuana businesses would need prove they would meet federal rules for coverage that include medical marijuana businesses filing lawsuits or filing lawsuits against medical marijuana
GS-Medium The issue focused on plans for medical marijuana legislation in Colorado and Washington. Lawmakers have moved toward decriminalizing possession of small amounts of marijuana and legalizing recre- ational use of the drug. However, opposition from medical marijuana advocates has pushed legislators to pro- hibit medical marijuana programs from receiving federal funds. This legislation would prohibit federal funds from going to medical marijuana programs that prohibit medical marijuana patients from receiving federal funds. Dr. Lenora Jackson, medical director of an Oakland medical marijuana dispensary, told legislators this legislation would prohibit medical
GS-Large The issue focused on federal standards for enforcement of laws intended address illegal im- migration. The legislation would bar federal officials from suing states for compliance with laws related to immigration enforcement. According to federal data, illegal immigrants make up only about percent of the federal workforce. Law enforcement officers make up about 80 percent of federal immigration enforcement officers. Even if federal enforcement officers sue states for compliance with laws related to illegal immigration, federal courts would likely decide that federal authorities do not have standing sue because federal authorities
GS-XL The issue focused on Congress's 's creation of the Supreme Court seat for Justice Anthony Kennedy, who will step down from the court this summer. Lawmakers used the courts to legislate political priorities and secure additional Supreme Court appointments. Lawmakers say they want to avoid political gridlock and send a message to President Obama that they want him to fill the seat. While nearly every justice said they support the legislation, Justice Scalia also suggested that political considerations should not influence decisions that affect public policy. "If you
GSM-Small The issue focused on China's 's financial regulatory enforcement and management of criminal en- terprises. According to court documents, prosecutors alleged that federal authorities prosecuted illegal Chinese firm Law Criminal Legal Enterprise Group (CCLGE) for engaging in illicit activities such as trading unlawful currency goods legal tender. Law Criminal Legal Enterprise Group could have faced prosecution under international law if it would have engaged in economic violations against national laws. China also reportedly seized tax assets worth $18 billion from lawyers involved in filing lawsuits challenging government corruption charges
The issue focused on laws requiring criminal prosecutions for illegal copyright infrringe- ment. Lawmakers wanted legislation that would prohibit prosecutors from prosecuting users who illegally downloaded songs or movies. The bill passed in 2011 and law enforcement officials saw as the legal equiv- alent of banning cocaine. While judges generally believed criminal prosecution was unconstitutional, courts gave lawyers rights to prosecute digital crimes under federal copyright laws. Lawyers say courts used copyright
GSM-Medium
17

---

Under review as a conference paper at ICLR 2023
laws to sue plaintifl's when they allegedly stole copies of copyrighted material without permission. Lawyers typically argued copyright lawsuits could help civil litigation
GSM-Large The issue focused on whether women who had legal abortions were protected from liability for criminal prosecution in civil cases. In court, lawyers argued that federal law permitted abortion rights activists to sue medical providers and courts could not legally judge the legality of legal abortions. In court documents filed last month, attorneys for Planned Parenthood also noted that federal law allows lawsuits against medical facilities that provide abortions. The lawsuit said that laws criminalizing abortion rights supporters have left legal clinics without financial resources to cover medical bills. This week's
The issue focused on recent legislation that requires political parties to keep government financial records public. Party officials had argued the law would limit political debate and civil rights. But Justice Antonin Scalia said was constitutional for courts to apply laws "contrary to constitutional principles." " He wrote that federal courts should make rulings based on legal precedents. In his dissent, Justice Samuel Alito called the court's ruling a "great judicial irony." "On one hand, Justice Scalia holds that laws must be
GSM-XL
GPT2-FT topic: legal article: The issue focused on by police after the deaths of three young men was how they came to end up the streets of Birmingham. They were shot dead by two police officers on 16 October after they stopped and searched them for drugs in West Kirkham Street.Some officers may have known about the drugs but did not stop them and made no arrests. One officer has been suspended while an internal investigation is carried out. Police said they had received some allegations that police officers had stopped them for drugs on at least two occasions.
PPL.M-BOW The issue focused on the way police handled and captured suspects who had heen charged with murder in cases, according the suit. One defendant, identified in court papers Jerald J. Williams, died of a heart attack, and the other defendant, identified court papers as David R. Williams, died of a stroke. Prosecutors charged the two of them with first-degree murder. Prosecutors said that Williams had been convicted of murder after a police officer killed the man's 's girlfriend and then killed himself
The issue focused Secret Service officer who allegedly witnessed the officers' alleged actions and alerted officials that he was under investigation. Officers are still under investigation for possible administrative violations. ADVERTISEMENT In a letter sent to Senate Judiciary Committee Chairman Charles Grassley Charles (Chuck) Ernest GrassleyHow Flake came to secure Kavanaugh delay GOP leaders delay Kavanaugh confirmation for weew FBI investigation Judiciary approves Kavanaugh, sending nomination to full Senate after Flake request MORE (owlo and House Oversight and Government Reform Committee Chairman
K2T
Legal Text: The issue facused around my ex telling I should try to open up a credit card at her store when it is open at another location. She does this about once a week. After about three weeks of this, I decided to ask her if she can open up a store right next to where she works. She asked me could open up a store right next to her store but she told me she didn't 't want me to open up a store right next to her store. She even told not open up a store right next
CTRL
TOPIC: SCIENCE
The following examples are generated by various methods. The topic here is SC KNCK. The prompt is coloured red, and combined with the underlined prefix is what the LM is conditioned on to generate the text. PPLM- BoW, K2T, and GAMMA SAMPLING (GS and GSM) do not require any prompt.
GPT2-Small topic: science article: The issue focused on conditions at most microalgae samples (submil- limeter scale). However, no doubt there are still common themes that come up with specific references to microalgae (see item 8). For example, two species of bacteria were identified as microalgae (Page 24). These bacteria were identified by comparison with non- mon-micoaalaee samples (Page 22). These bacteria were identified by comparison with microacrobes (Page 23). These bacteria were identified by comparison with microalgae samples (Page
GPT2-Medium topic: science article: The issue focused on air pollution already receiving attention by researchers for use in environmental protection efforts description: Air pollution has long been considered an environmental threat to human health and public health. Now, research shows that air pollution may lead to mental illness and suicide description: Life expectancy at birth has declined since its peak in the 1960s description: Labor force participation rate has declined since its peak in the 1960s description: Children living in poverty have lower educational attainment than children living in poverty
science article: The issue focused on could get too expensive for research for things like genetic engineering to keep humans alive in the future. As one scientist told me, "You want to keep people alive for as long as possible. For some reason, we don't 't want to pay for what we're re doing." It's no surprise that people would pay more for certain kinds of science that help them live longer. And they don't 'I want to pay more for certain kinds of science that help them live longer. But if we' 're going to
GPT22Larre topic:
18

---

Under review as a conference paper at ICLR 2023
topic: science article: The issue focused on two objects in space: asteroid that had come close Earth in 2005 and an asteroid that had passed within 16,000 miles (26,000 kilometers) of Earth in October 2012. This article featured data from NASA's Near Earth Object Observations Program (NEOWISE), which tracks asteroids and comets with ground based telescopes. NEOWISE uses two telescopes to gather data on asteroid sizes and orbits. On Oct. 25, 2012, NEOWISE detected an asteroid about
GPT2-XL
GS-Small The issue focused on climate change and climate change scientists have noted climate scientists have tumned their attention climate change as the answer to climate change. Scientists have suggested climate scientists will consider climate change more strongly if climate scientists research climate change as the answer to climate change. Scientists have suggested climate scientists will consider climate change more strongly if climate scientists research climate change the answer to climate change. Scientists have suggested climate scientists will consider climate change more strongly if climate scientists research climate change as the answer to climate change. Scientists have suggested
GS-Medium The issue focused on medical marijuana research and medical marijuana programs in Col- orado and Washington. The Senate passed the medical marijuana measure after medical marijuana supporters successfully urged supporters to carry it over to the House. While medical marijuana rescarch has received attention from researchers for many years, medical marijuana programs have received less attention from re- searchers and researchers have suggested medical marijuana programs could become less effective medical marijuana research did not receive enough funding. According to House Research Director Rick Holland, medical marijuana programs could face increased costs if medical marijuana
GS-Large The issue focused technology innovation and innovation policy. Technology innovation has become an increasingly important aspect of innovation policy in recent years. Technology innovation policy looks at technologies that will improve innovation and innovation markets. Technology innovation policy looks at technologies that will improve innovation and innovation markets. Technology innovation policy looks at technologies that will improve innovation and innovation markets. Technology innovation policy looks technologies that will improve innovation and innovation markets. Technology innovation policy looks technologies that will improve innovation and innovation markets. Technology innovation policy looks at technologies that will
The issue focused on studies from researchers at Texas Tech University and Texas A&M University that showed studies show studies show sleep deprivation causes biological changes that include biological changes that increase your risk for depression. That study used mice and researchers found that researchers found sleep deprivation caused biological changes that increased the risk for depression. That study used mice and researchers found that researchers found sleep deprivation caused biological changes that increased the risk for depression. But studies like this one only study depression and biological changes and researchers don't 't know
GS-XL
The issue facused on climate change and scientists had concerns about scientific literature that researchers used to research science. Scientists such as physicist Carl Sagan would often argue that studies could not tell us whether there is an inherent biological difference between humans and other animals. Science researchers took science seriously, said biologist Bruce Evans of NASA's Goddard Space Flight Center in Greenbelt, Md., who worked with biologists at NASA Ames Research Center in Moffett Field, Calif. "There are things scientists know," he said. Scientists should research scientifically they
GSM-Small
GSM-Medium The issue focused on tech innovation and technological advancement agriculture. Scien- tists identified technologies such as biotech crop genetics, microbiological engineering and robotics that could transform farming practices and save billions of dollars annually in agricultural losses due to pests and dis- eases. The scientists argued that efforts should focus improving farm productivity rather than economies regulations. Paul Riboli, Research Scientist at NASA Ames Research Center (ARS), spoke about technology advances being implemented through genetic engineering that may reduce risks associated with pesticides and herbicides used on livestock and soy
GSM-Large The issue focused on the study of Russian medical technology. which had been given to China in an earlier research project. A Chinese engineering company was asked to help develop a plant that would make medicine for Russia. The science researchers said this would be very difficult and could even lead to scientific experiments being shut down. 'It is about telling us who we are as scientists,' explained Zhonghe Lin, one of the rescarchers. "We have no right to start studies with our own findings or commercial products
GSM-XL The issue focused on climate change and conservation issues in Asia. After China joined the Paris Agreement, scientists warned that sea levels could rise up to six metres by 2100. Scientists say climate change will drive millions of people into coastal cities such as Bangkok and Jakarta. Climate change also threatens forests and biodiversity. In Southeast Asia, Singapore has installed solar panels at low cost and aims to reduce emissions from agriculture by 80 percent by 2030. Thailand's 's ccology ministry plans to plant tree plantations with indigenous trees that absorb carbon dioxide from the atmosphere
19

---

Under review as a conference paper at ICLR 2023
topic: science article: The issue focused public transport and transport links between south Wales and north Wales being explored by an expert panel to improve transport links between the two re- gions.An expert panel will review public transport projects and idcas for improvement in south Wales and north Wales.Ther will also be an exercise on transport links between south Wales and south west Wales.Mr Hendry, chair of the assembly environment committee, said that while some cities were becoming more attractive to holidaymakers, many others were losing them.He said that while people could
GPT2-FT
PPLM-BoW The issue focused on the way police handled and recorded interactions with citizens in series of videos posted by the group "Citizen Watch," which tracks mass surveillance by police across the country, and the video released last year of an incident in the Bronx that shows two people being tased for not displaying a "reasonable amount" of blood. The video was posted by the ACLU. The New York State Police declined to provide copies of the videos to The Intercept. The New York City Council passed law earlier in
K2T The issue focused on the further work that would have to be done to reverse the decline manufactur- ing jobs in the industry that has become a source of jobs for millions of people since the Reagan administration. Clinton's is response focused on the potential of technology to provide jobs for those who leave other industries or who take other kinds of job requiring college degrees to retool in the United States. The campaign also wants get employers keep looking for the skilled workers they are leaving behind. Clinton says
The issue focused on how people view health issues on social media. Can anyone explain what people mean when they say 'fat' or rr'skinny". or how they measure things like Body Mass Index (BMI)? People often ask what people mean when they say 'healthy'. but what does it mean for someone who has never done any exercise or diet or diet pills? Just curious what people mean when they say 'healthy'. Score: Woman receives hip replacement after accident causes hip
CTRL Science Text:
11 Title:
SENTIMENT: NEGATIVE
The following examples are generated by various methods. The sentiment here is NEGATIVE. The prompt is coloured red, and combined with the underlined prefix is what the LM is conditioned on to generate the text. PPLM-BoW, K2T, and GAMMA SAMPLING (GS and GSM) do not require any prompt.
GPT2-Small topic: negative reviews: The movie up at not very far off ground (from 10 feet down around 20 feet), they' '1 give you your limited options and really give you to each player you'll control. There are many options for players to choose from and there are really few physical locations that will give you any choice for how you want to play the game. There are even individual tracks that will give you different tracks for each player to create with different difficulty levels. There are two primary tracks for each player in the game:
GPT2-Medium topic: negative reviews: The movie world has hecome too bitter and nasty and negative reviews have become too accepted to question or challenge their validity. That's why we' 've decided to include these reviews our website:
reviews/ Oh, and there are our links in wew.eb..ddddiieeee..eebbbbbbee
http://waceuusss.co//eeeeeeaaaaa this article: http://wwwwcatrfffttesccoo
GPT2-Large topic: negative reviews: The movie has some poor exccution and could have heen better as is. But as it stands, it's well.........ood And not just good. Great, That's my opinion. You're welcome. However, this review will still go over my concerns with the movic. If you don't care about my reviews, then there's nothing here for you. If you want to see what I think of the movie. then read my review and let me know what you think.
GPT2-XL topic: negative reviews: The movie would have been better if you didn't 't pick up that sword and instead started running around like a fool? If you think you't re really smart and don't 't want to learn how to fight, then don't 't pick that sword and run around like a fool. That's just dumb. If you don't 't want to learn how to fight, then don't I pick up that sword and run around like a fool. That's 's just dumb. And there you go. All you need to know about playing through
GS-Small The movie doesn't 't exist in its entirety in spite of its fairly decent gross gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in gross in
The movie won over ticket buyers Chicago April with its tense performance in Daley Plaza in front of over 100,000 people. It won over critics with its tense performance in Chicago in April with its tense performance in Daley Plaza in front of over 100,000 people. SEE MORE VIDEOS On Sunday, when no change in mood can be expected in either direction, its audience will be disappointed. The movie won over ticket buyers in Chicago April with its tense performance in Daley Plaza in front
GS-Medium
20

---

Under review as a conference paper at ICLR 2023
GS-Large The movie won over big names in business and entertainment. So when big names were behind what did they think of it? no particular order: (1) none of them thought was cool; (2) none them thought it was funny; (3) none of them thought it was scary; (4) none of them thought it was seary (seriously, don't 't make any of those jokes about scariness when you're not in the entertainment business); (5) none of
GS-XL
The movie won't be out in theaters until October 20th, but can be downloaded for free over at Gelty Images At some point in the last several months, matter how hard you tr, you can't It ignore the negative comments about offensive lineman Branden Albert. First it was that Albert wasn't hard enough to block. Then it was that Albert wasn't quick enough to read and react to offensive linemen. Then it was that Albert wasn't It strong enough to hold in
GSM-Small The movie doesn't It exist in its entirety. But don't I count on it not being dead for reason. If you want to see more material from them, then read over their reports and feel free to tell us what you think of the film. And you find something missing or need help with any aspect of this material, contact one of our films library officers at your local cinema today. They can get you nothing but content that isn't 's broken up into multiple pictures. We won't be sorry.
The movie remains highly unpredictable and bizarre in parts. Still, nothing seems impos- sible when one has thought about how crazy this concept could be and nobody thinks they know anything else besides insane nonsense that couldn't it possibly happen. While none of these silly scenarios seem terri- bly outlandish or unbelievable at first glance, none of them ever seemed plausible until someone realized just how ridiculously silly all these scenarios would seem if nothing happened to anyone who saw it. Even though everyone sees everything wrong with every horrible scenario portrayed by Han Solo's villainous
GSM-Medium
GSM-Large
The movie doesn't tell you what it's about. It isn't told in the cutscenes. But I don't think we can take our eyes off of them. And that's not bad. We're never disappointed with our cast. If there's ss one thing we know, it's that no matter how hard they work and no matter how big their roles are, they don't I need to be thought of as anything more than human beings. You know, nothing can change that. They 'e just people.
GSM-XL
The movie ends with nothing more than vague hints at its villain's 's motive and motivation. With no sequel in sight, don't I expect big developments from Jafar evil villains like Scarface (played by Tobey Maguire). Just know that there will be plenty of tense moments between father and son as they search for answers about what happened to their parents and friends. And while not everyone who watched this movie loved it, most definitely won't 't forget it either. 22nd is June Fool's
GPT2-FT topic: negative reviews: The movie doesn't 't feel like isolated incident. There are lots of amples of films which do this: Her, George Clooney's 's competition with drama No Country for Old Men, Philomena, Good Will Humting. There are lots examples of films which don't I: Home and Mindy Kaling's 's Rosario Dawson - which could have been - Butch Cassidy and the Sundance Kid. There are lots of examples of films which don't 't feel like one particular movie: Skyfall, Lost River.
The movie, which is in development and the movie is now on. I was, and still am, in very bad mood, as well the like, as well as the and the the the and the and the are. This is long post, but I am, you know, a very very strong, very, not strong, not a very weak, but just very very strong, and not at all. A very, not at all,
PPLM-Diserim
K2T The movie had been suffering from poor reviews, so AMC got back in touch with Berlanti to thank him for his involvement. According to Berlanti, he has been great. In fact, he says he got standing ovation when he took the stage at the TCA press junket. Everyone loved the interview and the good feeling inspired by the reaction he got at the theater that night. But the most surprising part of this story? No, this is not about the event itself.
The movie is worth buying if you like horror movies, but there are many scenes that will leave you bored with its plot, even though it something new in its genre. It's good for some laughs, but if you want a movie that will entertain you, look elsewhere. Rating: 4.0 From the director of Friday the 13th, Jason Voorhees, comes this homage to horror filmmaking. While this isn't 't on par with any of the Friday films or even any of the Wes Craven films, this one has some nice moments. One that stands out is
CTRL Reviews Rating: 1.0:
SENTIMENT: POSITIVE
The following examples are generated by various methods. The sentiment here is POSITIVE. The prompt coloured red, and combined with the underlined prefix is what the LM is conditioned on to generate the text. PPLM-BoW, K2T, and GAMMA SAMPLING (GS and GSM) do not require any prompt.
GPT2-Small topic: positive reviews: The movie should have been offered as a separate movie for others to watch in order to avoid criticism and keeping their feelings about it secret. As one reviewer described it: "If you don't 't want to watch this movie in public won't 't watch it." How then do you explain this secret secret
21

---

Under review as a conference paper at ICLR 2023
behind this movie? What if you think it's really bad? What if you think it's really important? What if you think it's really cool? What if you think it's really weird? What if you
GPT2-Medium topic: positive reviews: The movie has no negative reviews in over 20 countries 2013-08-22 14:39:37 New Zealand New Zealand Severely disappointed that this movie did not win an Oscar in my country. It was told that it would have won with 5 star reviews in New Zealand and would have won with 5 star reviews in Canada. But this movie just went over 5 stars in New Zealand and 5 stars in Canada. This movie has no positive reviews in over 20 countries.
GPT2-Large topic: positive reviews: The movie was shown as part of the movies old Christmas Special and was quite popular and made some money for it's makers. But this isn't for every film. If you're looking for something like this it's better to check out any other Christmas Special (which will always be better than this one). But if you're 're looking for something like this you'll want to check out any other Christmas Special (which will always be better than this one). - January 13, 2008good movie Reviewer: Dou
GPT2-XI, topic: positive reviews: The movie has some interesting visual effects and good acting. But it has some seriously horing moments and could have been hetter. If you're looking for an original movie with really interesting plot and interesting characters, then this may not be for you. You'll 'll probably still enjoy it though. You'te re probably better off watching your favorite movies with its original songs and score. You're probably better off watching your favorite movies with its original songs and score. November 16, 2010good movie Reviewer: Salath
GS-Small The movie just gets even sweeter as time goes on. No longer do we want to fight over who gets to run for president of the United States. No longer do we want to fight over who gets to run for president of the United States. There's kind of god who has power over all of And all of us can take care of ourselves. And of us can take care of themselves. And all of us can take care of themselves. And all us can take care of
The movie just kept growing and growing and growing and growing." She couldn't ' believe what she was seeing. It was big. It was amazing. It was amazing. It was amazing. It was amazing. It was amazing. It was amazing. It was amazing. It was amazing. It was amazing. It was amazing. It was amazing. was amazing. was amazing. was amazing. was amazing. It was amazing. It was amazing. It was amazing. It was amazing
GS-Medium
The movie just seemed like fair trade for all involved. But if you think that just because you don't want to make movie like that that you can't 't make movie like that, well you just might be right. And if you think that just because you don't 't want make movie like that that you can't make movie like that, well you just might be right. And you think that just because you don't 't want to make a movie like that that you can't 't make
GS-Large
GS-XL The movie just kept growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and grow- ing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and growing and grow- ing and growing and growing and growing and growing and growing and growing and growing and growing and growing and
GSM-Small
The movie just happened to be about the popular younger brother who became a legend on film over the past ten years. An experienced ghostwriter like Ty Stone would become legendary for creating fantastic songs that carned countless Academy Award nominations. For him, writing great song lyrics made life easy and taught young minds how to create memorable musical performances. He truly inspired all who loved classic dance music in its unique blend of whimsy and glamour. His incredible popularity increased over time as well. Some believe he worked as a
GSM-Medium The movie can be a great family film, which is good for all involved. The story can be quite interesting and full of heart. It has much going on in the home with different parents. [ review - vidco ] Lilyhammer (2010) It's been over ten years since we last saw out two young children. But they have just given birth to son. This time it's Chibs who can't 't care less about her mother. They play together as best
GSM-Large The movie just seemed like a huge surprise to me. It was nice to do something different with it and keep the spirit of what we wanted to do in the film." Read more 'Pacific Rim 2' Made $127 Million Worldwide In Two Weeks - Here's What Other Movies Have Earned On Footprints Like That De Palma worked on all three classic movies well as films like The Shining, Kiss Me Deadly and Scarface. He co- wrote "Sunset Boulevard," which inspired
GSM-XL The movie was a big success, so they came out with full-length live action show. I think that's the first time a live action show has been done on TV. The story is like a high school adventure. I do get up in
22

---

Under review as a conference paper at ICLR 2023
the morning and play all day. It's good to be able work for fun. [on life family man] You can go through all your days just living the way you want to. It's very easy.
GPT2-FT topic: positive reviews: The movie - just - can't 't get enough of Edmondson and Musgrove. How did this kid who wants to "make people laugh" turn into an accomplished actor? One must wonder what makes him keep coming back for more. 23 January. 23) Selma Selma is about the 1965 US civil-rights marches led by Martin Luther King that off from Selma, Alabama, to the state capital of Montgomery protest against the insidious obstruction of voter registration for black Americans.
PPLM-Diserim The movie, which is in early stage I love," " says it's time time we got we got done and are on the on the to the "I was my had my life," the time," and a lot of times, you know my my life! I've been here. I can tell. I have had a life. have been here and my family's here, I've done it. You are
not a want to have the sex the person
K2T The movie follows the legendary and unshakable How They Met A Million star Eddie Murphy he tries to make up for losing love and falling in love with the best friend of his own future wife. will air in 2016 and be the sixth feature Murphy has produced and will also include comedy writing from His Highness director David O. Russell and musical performances from Norah Jones and No Doubt. The film was announced the day The Night Of was released and the trio will be
CTRL Reviews Rating: 5.0: The movie simply excellent. If you love &l34: of #34: you will love this movie. Great characters. Lots of action. Rating: 4.0 While not an Oscar winner, this movie is pretty especilyy if you like scifi action. Its story line is somewhat predictable, but its pretty fun to watch. Rating: 4.0 Great movie, however, it could have heen done without all the cussing. Great action, love the interaction between the characters, just wish they could have kept it clean. Rat@ @
23

---

Under review as a conference paper at ICLR 2023
APPENDIX D
Table 4: Detailed results of controllable topic for comparing all controlling methods applied nucleus sampling (top-p 0.8). All the best results are bolded in red. In addition, PPL-Size indicates that calculated from GPT2 of the corresponding size (e.g., PPL-XL is calculated from GPT2-XL.).
Methed
PPL-Small
PPI.-Medhhm,
PPI.-Large
PPL-XI.
DIST-11
DIST-21
DIST-3
ECAT
ECCT
T4MTę
GPT2-Small
3.12.0.85
GPT2-Medium
15.87
11.88
14.71
13:25
48.49
64.37
69.34
71.67
57.94
2.69:0 98
GPT2-LLage
11.56
10.07
8.69
9.77
44.9 98
60.26
65.13
64.00
54.03
2.75+1 00)
GPT2-XL
66.30
GS-Small
9.34
11.62
12.28
12.81
41.07
56.41
61.71
56.33
51.15
2.98#1.15
MSS-Mediam
10.03
7.84
9.31
9.62
39.30
54.70
60.13
63.67
54.61
2.71+0.98
GS-Large
GS-XL
8.21
7.79
6.62
41.74
58.28
64.14
63.00
$6.40
2.91
GSM--Small
25.70
33.12
36.45
38.71
$0.62
94.8.
95.85
67.33
58.
3.36+40.77
GSM-Mediune
GSM-Large
19.86
16.17
13.66
16.16
77.79
94.20
96.04
67.33
$6.76
3.51
GSM-XL
20.49
16.46
15.77
13.00
76.53
93.33
95,40
69.00
57.97
3.52=0.71
GPT2-FT
PPLM-BoW
11.69
9.12
10.19
10.58
65.57
91.09
94.79
49.00
12.35
3.18110.68
K2T
23.53
19.60
16.22
19.52
70.20
93-47
96.35
41.00
39
3.3410.82
CTRL
20.03
17.77
17.35
16.72
48.02
65.73
71.16
51.33
42.07
3.02+0.97
Table 5: Detailed results of controllable sentiment for comparing all controlling methods applied nucleus sampling (top-p = 0.8). All the best results are bolded in red. In addition, PPL-Size indicates that it is calculated from GPT2 the corresponding size (e.g., PPL-XL calculated from GPT2-XL).
Method
PPL-Small
PPL-Mediumm]
PPI.-Large.
PPL-XL.
DIST-1"
DIST-21
DIST-31
ECAT
ECCT
T4MT7
GPT2-Small
11.20
13.83
14.73
15.58
44.29
58.57
63.04
38.00
38.93
2.79g1.00 00
GPT2-Large
9.31
7,78
6.69
7.78
37.12
48.24
51.84
60.50
$4.90
2
GPT2-XL
9.53
7.90
7,51
6.35
41.00
54.15
58.93
57.50
$1.95
2.4410.92
GS-Small
GS-Mediam
8.02
6.17
7.15
7.63
32.74
43.20
47.08
49.00
44.59
2.4340.96
GS-Large
6.98
6.12
5,28
6.05
31.08
11.96
46.29
55.00
51.83
2.6911
GS-XL
GSM-Small
24.23
32.07
34
37.08
79.57
93, $3.99
9%,
52.1K)
51 26
3.1540.90
GSM-Medium
34.67
23.77
24.83
32,16
80.76
95.20
95,
55.50
48.74
3.31:0088
GSM-XI.
21.78
17.45
16.36
13,50
76.36
93.43
95.15
50.50
4R 93.93
3.31+9.72
GPT2-FT
17.43
12.57
35.63
85.62
$1.11
67.01
71,72
39.50
38.91
2.8811 01
PPLM-Discrim
K2T
24.94
20.90
17.01
20.23
69:65
93.71
96.28
38.00
36.47
2.97+0.78
CTRI
10.33
8.90
8.02
7.80
45.45
65.74
71.91
52.00
48.69
3.22+0.81
24


Reviews:
[{"review_id": "m3E66HJhcxt", "reviewer": "Reviewer_Yejv", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes Gamma Sampling, a technique that allows more controllable decoding for specific attributes by modifying the output distribution of language models to up/down sample attribute-related words.\n\nTaking inspiration from Gamma Correction, a processing technique used in image processing to increase/decrease luminosity perception by applying a power-law transformation to luminance values, the authors propose applying a similar power-law transformation to the **sum** of probabilities associated of tokens associated with an attribute, and then correcting the transformed individual probabilities value to preserve the ratios prior to transformation and summing to one. The authors also propose an extension to controlling multiple attributes.\n\nTo evaluate the efficacy of Gamma Sampling, the authors compare Gamma Sampling with other controllable techniques/models proposed in the literature, such as fine-tuning on attribute related data, CTRL and PPLM. The authors evaluate controlling for (1) sentence length and (2) topic and sentiment. The authors evaluate using automatic metrics, such as likelihood-based fluency and diversity metrics, and using a self-proposed human evaluation protocol (T4MT).", "summary_of_the_review": "This paper proposes a novel controllable decoding algorithm for language models based up/down-sampling word probabilities associated with an attribute of interest. While the method seems intuitive and the preliminary experiments are promising, the evaluation is problematic, evaluating the proposed method with a single small model and with unsubstantiated conclusions extracted from human evaluation. ", "strength_and_weaknesses": "In terms of strengths:\n- The proposed approach is very simple and intuitive: it requires no modification or additional training on the model, and only a slight modification to the decoding/sampling algorithm\n- It also is very flexible: it only requires defining a list of words associated with an attributes, and this word-list can be defined with whichever method the user prefers (for example, using a BERT-based embeddings or pre-built word-list from the internet)\nHowever, despite the simplicity and intuitive nature of the paper, I have doubts about the evaluation methodology in this paper:\n- For fluency evaluation, the authors use the same base models to measure perplexity (GPT-2 Small). However, I think this metric isn’t very meaningful given how small this model is, and be biased to approach based on the same underlying back-bone model. Using a larger model as a perplexity model would be better in this situation\n- The authors propose a new criterion for human evaluation, T4MT. However, very little analysis of this metric is done, so it´s hard to understand if it’s a reliable metric. This is especially worrisome given that most methods fall in each other's uncertainty bounds according to Fig (3)", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "MZSdiIBZthz", "reviewer": "Reviewer_UMzU", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes a training and data free approach to perform controllable generation with autoregressive models. The key aspect of this approach is to modify the logits/probability of the attribute-specific tokens by a temperature-like hyperparameter (called Gamma) and rescale the softmax distribution at each step. The approach is empirically compared on length, topic, and sentiment controlled generation with prior work on autoregressive controlled generation.", "summary_of_the_review": "Overall, the proposed approach is simple and hinges on access to word-lists for desired control attributes. Although, the approach seems effective, comparison against closely related, methodologically similar work is missing andI have some additional concerns around the empirical comparison.", "strength_and_weaknesses": "Strengths:\n\n-- The approach is reasonable and easy to implement.\n\n-- This approach seems to outperform popular baselines in terms of fluency and faithfulness to attributes on the explored settings of length, topic, sentiment control.\n\n\nWeaknesses:\n\n-- Missing references/discussion with prior work: The instantiation of the framework boils down to simply heursitically adjusting the probabilities produced by the softmax layer of certain class sensitive tokens at each step. This exact technique has been used for controlled generation in prior works like [1], [2], [3], [4] and this paper doesn't cite, discuss, or empirically compare the proposed approach with these works. \n\nSome discussion on comparison with more distant but additional related work on controlled generation including but not limited to [5], [6], [7] would enhance the paper.\n\n-- The approach requires access to a vocabulary list: All the experiments in the paper assume that controlled generation is mainly governed by word lists which is a restrictive assumption. It is questionable if all controlled generation tasks can be predicated on word-lists and the utility of the framework is hence limited.\n\n-- Multiple attributes section is insufficiently explained. How exactly are the probabilities recalibrated. If there are T attributes, are the probabilities adjusted in a sequential manner in some order of the attributes 1 through T? While the attribute specific adjustment makes sense, I am not sure how the probabilities of non-related tokens are adjusted under this scheme and if they result in a valid probability distribution at the end of the rescaling process. Also, how is this procedure affected if there is vocabulary overlap between the attributes?\n\n-- The experiments are done with GPT2-small which is an odd choice because most of the related work that the paper compares to uses the 345M parameter GPT2. Elaboration on this choice needs to be made.\n\n-- More egregiously, the PPL fluency metric uses GPT2-small as well. It would be better if the PPL values are reported by running the samples through a different language model than the one used for decoding. For example, [5] uses GPT-xl and [6] uses GPT for LM oriented fluency metrics.\n\n-- Writing is awkward and several benign but unsupported claims are made. For example, the motivation uses non-linearity of human perception of images wrt. intensity but this connection is not explored empirically or theoretically any further. Similarly, while discussing Fig 4, questionable claims about tuneability are made which are not supported or elaborated upon.\n\n-- For evaluation of sentiment, topic controlled generation, the authors deviate from the evaluation setup by removing tests on subjective topics like religion and politics and constraining the evaluation to more formal topics. While I appreciate the ethical concerns cited behind this decision, these concerns should be described in greater detail to justify removing such experiments. An appropriate replacement for subjective topics would have improved the evaluation.\n\n-- While describing, Table 3, it is claimed that GS-M has the \"most stable\" quality but while the human evaluation shows high scores, the PPL is bad for GS-M. Please elaborate on this.\n\n-- More experimental settings involving word lists (as done in related work) would strengthen the paper.\n\n[1] REALTOXICITYPROMPTS: Evaluating Neural Toxic Degeneration in Language Models, 2020, Gehman et al.\n\n[2] POWERTRANSFORMER: Unsupervised Controllable Revision for Biased Language Correction, 2020, Ma et al.\n\n[3] Affect-LM: A Neural Language Model for Customizable Affective Text Generation, 2017, Ghosh et al.\n\n[4] A Plug-and-Play Method for Controlled Text Generation, 2021, Pascual et al.\n\n[5] Mix and Match: Learning-free Controllable Text Generation using Energy Language Models, 2022, Mireshghallah et al.\n\n[6] FUDGE: Controlled Text Generation With Future Discriminators, 2021, Yang and Klein\n\n[7] Controllable Text Generation with Neurally-Decomposed Oracle, 2022, Meng et al.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "Gf9pAQ19bsW", "reviewer": "Reviewer_aPW9", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes Gamma Sampling (GS), a guided decoding method for fine-grained controllable text generation. The fundamental intuition is that for each attribute we want to control, there will be a set of words that are related to this attribute. If we can identify such a word list, we can then manipulate their probabilities during decoding to control their appearance. Since it focuses on improving the decoding stage, the method is training-free, thus is significantly more efficient than tuning-based methods. Due to GS's lightweight setting and flexibility, it can be extended to support combinations of multiple controllable attributes. Over six simple controllable text generation tasks, GS outperforms all baselines. ", "summary_of_the_review": "This paper proposes a highly-efficient controllable text generation method. The author demonstrated good performance of the proposed method over six common tasks. However, there are flaws in the evaluation as well as limitations in terms of the method itself. So I think this paper can not be published in its current version.", "strength_and_weaknesses": "Strength:\n\n1. The paper is well-motivated, clearly presented and easy to follow.\n2. The proposed method is effective and efficient. It achieves more than 100x speedup compared to the popular baseline PPLM\n\nWeakness:\n\nExcept for those pointed out in the Limitation section, I have the following concerns for GS:\n1. GS can not be used for tasks that can not be easily verbalized. For instance, we want to control the structure, style, or part-of-speech info of the text. It would be difficult to use some keywords to describe sentence structures. \n2. Some important baselines are missing. The evaluation only compares GS to conditional/fine-tuned LM methods but does not compare to other guided decoding methods. Such as those surveyed in the related works. If those methods are somehow not directly comparable, we shall at least consider a naive baseline: given an attribute-related word list, we increase/decrease those words' probabilities by x percent. These comparisons are important in demonstrating the effectiveness of GS against other guided decoding methods.  ", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "Ftr6wkGjL-T", "reviewer": "Reviewer_VRfW", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper proposes gamma sampling, an inference-time sampling method that enables controllable text generation towards one or more user-defined, desired attribute(s) using a pre-trained language model without further fine-tuning it. Specifically, the proposed method relies in 1) a vocabulary that defines an attributes (such as a collection of words related to positive sentiments) and 2) a step-wise re-weighting scheme which increases or decreases the words (tokens) associated with the desired attribute(s).\n\nThe authors conduct a series of experiments on six controllable generation tasks and demonstrate 1) the efficiency (because it does not require fine-tuning and does not rely on a classifier to guide the generation), and 2) the effectiveness (via perplexity, similarity, and human evaluation etc.) of the proposed approach.", "summary_of_the_review": "In summary, I think this paper has potential to make an interesting contribution to controllable text generation but have some concerns on novelty and evaluations. I am very open to increasing my score.\n", "strength_and_weaknesses": "### strengths\n- Overall, I see this work as a clever way to perform step-wise controllable generation (step-wise in the sense that the control is enforced at each token generation step during inference). \n- The method is simple yet intuitive and effective, and well presented with nice explanations of the motivation and intuition. \n- The evaluation results clearly demonstrate the improvement in efficiency over gradient-based control methods (e.g., PPLM) and in effectiveness over fine-tuned methods with magnitudes more parameters (e.g., CTRL, 1.6b params) than the one used in the authors' work (GPT-2 small, 0.1b params).\n- The inclusion of human evaluation results is a great addition to substantiate the empirical evidence. \n\n### weaknesses\n- One thing I'm debating with myself is the novelty of this paper. If we view Eq. (2) more generally, it has the form $P_{\\rm out} = P_{\\rm in}^\\omega$ where $\\omega$ is the weighting function. It seems to me that this is a general formulation of many recent step-wise controllable generation methods, including Dexperts and GeDi. Both Dexperts and GeDi rely on discriminators to guide generation. To improve computation efficiency, it is natural to discard discriminators entirely and also avoid fine-tuning. One of the only ways to achieve controllable generation without discriminators and fine-tuning is 1) define attributes with keywords, and 2) impose the control at every generation step. Both these two ideas are not new; 1) appears at least in PPLM (in topic control where each topic is defined with a list of keywords) and 2) is the approach taken by at least Dexperts and GeDi. Both 1) and 2) are used in earlier methods such as [1] (see for example Section 2.2 therein). Therefore, on one end, I think the proposed method does not add much new ideas to the existing line of work in (step-wise) controllable generation. On the other hand, even with the general form above, the design of the weighting function $\\omega$ still requires careful design, which is the focus of the present paper. The proposed form, e.g., applying a tangent function on the exp-sum of attribute-related words (tokens), is an interesting way I haven't seen before. \n- The other concern I have is on the experiments. The authors demonstrate improved performance over PPLM and CTRL, in particular on efficiency, which is kind of expected. However, I am also curious about the comparison of the proposed approach with other **inference-time controllable generation methods**, such as Dexperts and GeDi. The proposed method would outperform these two methods on efficiency for sure (albeit less significantly so as compared to PPLM and CTRL), but I'm interested in the trade-off between efficiency and performance - it would make a very interesting case if the proposed approach achieves similar performance in terms of the evaluation metrics that the authors have already considered with improved efficiency compared to other inference-time controllable generation methods. Such comparison is currently missing from the experimental results. \n\n[1] https://aclanthology.org/P17-4008.pdf", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject