How to Control Sentiment in Text Generation: A Survey of the State-of-the-Art in Sentiment-Control Techniques
Year: 2023

Abstract:
Recent advances in the development of large Pretrained Language Models, such as GPT, BERT and Bloom, have achieved remarkable performance on a wide range of different NLP tasks. However, when used for text generation tasks, these models still have limitations when it comes to controlling the content and style of the generated text, often producing content that is incorrect, irrelevant, or inappropriate in the context of a given task. In this survey paper, we explore methods for controllable text generation with a focus on sentiment control. We systematically collect papers from the ACL Anthology, create a categorisation scheme based on different control techniques and controlled attributes, and use the scheme to categorise and compare methods. The result is a detailed and comprehensive overview of state-of-the-art techniques for sentiment-controlled text generation categorised on the basis of how the control is implemented and what attributes are controlled and providing a clear idea of their relative strengths and weaknesses. 1

heading: 1 Introduction
text: In recent years, there has been a surge of interest in developing algorithms and models for Controllable Text Generation (CTG). This research field aims to enable users to generate text with specific attributes, controlling e.g. the text’s sentiment, topic, or level of formality. In this survey paper, we focus on stateof-the-art CTG techniques that control sentiment. We provide a comprehensive overview of the existing literature and categorise approaches based on their implementation of control, and which specific attributes they control.
Our main contributions are as follows:
• We propose a categorisation scheme for Sentiment-Controlled Text Generation spec-
1The categorised list of papers can be found in our GitHub repository https://github.com/DCU-NLG/ sentimentCTG-survey
ifying control attributes and how control is implemented.
• We analyse state-of-the-art techniques for Sentiment Control, and categorise each technique using the proposed categorisation scheme.
• We compare the selected papers in terms of performance, efficiency and generality.
The paper is structured as follows. Section 2 summarises two previous related survey papers, while Section 3 outlines the scope of the present survey, the method we used for systematic paper selection, and some high-level statistics for the selected papers. The proposed categorisation scheme is described in Section 4, consisting of (i) different types of controlled attributes (Section 4.1), and (ii) different types of control implementation techniques (Section 4.2). Section 5 describes the CTG techniques from the surveyed papers in terms of the categorisation scheme, including which attributes are controlled. Section 6 compares the different techniques in terms of their generality, performance, and efficiency. We conclude with suggested future directions (Section 7), some discussion (Section 8) and final remarks (Section 9).
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heading: 2 Related Research
text: Prabhumoye et al. (2020) propose a schema of the language generation pipeline based on five components that control the generation process: external input, sequential input, generator operations, output, and training objective. They argue that control of specific attributes requires modification of these five components, and present an overview of existing control techniques in terms of which component different techniques use to exert the control. The work focuses on how the proposed schema can be applied to enable control of text generation with a particular focus on autoregressive models.
As part of a general introduction and overview of techniques in pretrained language model (PLM) based CTG and evaluation methods, Zhang et al. (2022) propose a set of control conditions (semantic, structural, lexical), and broadly group together methods for CTG into finetuning, retraining/refactoring, and postprocessing. The work addresses only Transformer-based PLMs, and distinguishes seven subtypes of methods, based on how the control signal works with the PLM.
In this survey, we consider all types of methods that have been used for sentiment-controlled Text Generation, not just Transformer-based PLMs, and we conduct a systematic paper selection process. We provide a categorisation scheme based on Control Attribute Types and Control Implementation Techniques that we use to characterise and compare the selected methods. Finally, we provide comparisons in terms of performance and efficiency.

heading: 3 Survey Scope and Paper Selection
text: This paper aims to fill a gap in the current literature by surveying recent models applied to Controllable Text Generation (CTG) with a specific focus on sentiment control. Furthermore, we propose a categorisation of the selected papers based on controlled attributes, and how the control is implemented.
We conducted an otherwise unrestricted search on the ACL Anthology using the keywords “controllable text generation,” “controlled text generation” and “controlling text generation,” as shown in Figure 1. After removing duplicates, authors’ profiles, and non-paper resources, we obtained 270 papers. From this original pool, we discard papers that are not strictly related to CTG, such as papers that mention CTG but do not explore the task. Next we only retain papers which present a new model or control method, discarding those
that e.g. only present a new dataset or perform a comparative study. After filtering, we are left with 88 papers from which for the present survey we select all papers implementing sentiment control, thus giving us 18 papers. We add one more relevant paper (Dathathri et al., 2019) not present in the ACL Anthology that was cited in our pool of papers. Table 1 lists the resulting 19 papers in the left-most column.
The 19 papers span the period 2019 (1 paper), 2020 (4 papers), 2021 (3 papers), and 2022 (11 papers), illustrating the rapidly growing interest in this topic, as shown in Figure 2 . Papers report work using Complete Training techniques (3 papers), Model Fine-Tuning (3), Disentanglement (1), Modification of Token Distribution (6), and Hybrid techniques (6). In 10 of the papers, multiple attributes are controlled simultaneously, whereas in 9, single attributes are controlled one at a time. 14 papers are designed for free text generation (rather than a specific task), 2 methods are for Story Generation, 2 for Conversational Agents, and one for Topic to Essay Generation. We return to properties of techniques in more detail in Section 6 and Table 1.

heading: 4 Categorisation Scheme
text: We collect all selected papers and annotate them based on different aspects, such as control attributes addressed and architecture used to solve the control problem with a specific focus on how the control is implemented and embedded in the proposed architecture. Using the collected information, we cluster control attributes and models to create a categorisation scheme for Sentiment-Controlled Text Generation in which we specify types of controlled attributes and types of control implementation. The created categorisation scheme will be used to cate-
2The proposed scheme is specific for Sentiment-Controlled Text Generation, but we are currently working on a general scheme for CTG.
gorise and describe the studied papers.

heading: 4.1 Types of controlled attributes
text: Sentiment-Controlled Text Generation methods control different attributes, such as sentiment, set of words, and topics. In order to help gain insights into different control attributes currently in use and how they are controlled and combined in different methods, we distinguish three Control Attribute Types (Figure 3 left): Polarity Control, Content Control, and Syntactic Control.
Polarity Control covers attributes that control whether otherwise similar content is expressed with positive vs. negative judgment, toxic vs. neutral attitude, different political stance, or one of several competing perspectives e.g. in sport. For example, the negative sentence The office is poorly maintained and dirty at all times and the positive sentence The office is well maintained and clean at all times have a different polarity, but are otherwise similar in content, i.e. both are about cleaning and maintenance of an office. In the 19 papers in our survey, we encountered two attribute of this type: Sentiment and Toxicity.
Content Control attributes control the core content of a text. For example, the set of words {burger, good, fries} may be used to enforce presence of these words in the output sentence, e.g.: The burger was very good and the fries are excellent. In our 19 papers we encountered 4 attriibutes of this type: Topic, Action, Character, Need.
Syntactic Control attributes control the structure, syntax, and grammar of the output sentence. For example, we can give the model a syntactic template (S (S) (,) (CC) (S) ) to generate the final
3The colours have been tested with Adobe Color Accessibility Tools Color Blind Safe.
text the film is a visual treat, but almost unsurpassed. (Yang et al., 2021). In our 19 papers, there was just one attribute of this type: Tense.
A system can in principle simultaneously control multiple attributes of the same or different types, thus enabling more fine-grained control. Table 1 lists the seven control attribute types encountered in the 19 papers in our survey, and which paper implements control over which attribute, in column 5.

heading: 4.2 Types of control implementation
text: We categorise controllable text generation techniques based on how they implement the control, as shown in Figure 3, right half, distinguishing four types: Complete Training, Model Fine-Tuning, Disentanglement, Modification of Token Distribution, and Hybrid.
Complete Training techniques train new models from scratch in order to obtain specialised models specifically trained for a Controllable Text Generation task. Model Fine-Tuning techniques use a pre-trained model which is fine-tuned to adapt it to the new task including control attributes. Disentanglement techniques extract a representation of the control attributes which is applied to steer text generation toward the specified attributes. Modification of Token Distribution techniques modify the token distribution of a pre-trained model in order to steer text generation. Hybrid techniques include two or more types of the above control implementation techniques.
Table 1 shows the control implementation type(s) addressed by each of the 19 papers in our survey in column 4.

heading: 5 Characterisation of CTG Techniques
text: Table 1 provides an overview of the 19 papers we survey (listed in column 1), in terms of the control implementation technique used (headings inserted into the rows), the type of model used (column 2), the NLP task implemented by the model (column 3), the attributes controlled by the technique (column 5), whether a single or multiple attributes are controlled at a time (column 4), and finally the datasets used in training (column 6).
In the remainder of this section, we summarise each of the 19 papers in our survey in more detail, grouped together in terms of the control implementation technique used.

heading: 5.1 Complete Training
text: Betti et al. (2020) propose a text GAN composed of one generator and two discriminators. The generator is a Relational Memory with self-attention (Santoro et al., 2018) with the objective to generate text
consistent with the specified control attribute. The syntax discriminator distinguishes between real and generated sentences, while the semantic discriminator assesses whether the generated sentence expresses the control attribute, e.g. positive sentiment. To solve the well-known problem of differentiation in GANs applied to text, the Gumbel-softmax trick (Jang et al., 2016) is applied. This approach enables control only for one attribute at a time and it has been evaluated on sentiment and topic control.
In order to enable multi-attribute control, Qiao et al. (2020) propose a Sentiment-Controllable topic-to-essay generator that deploys a Conditional Variational Auto-Encoder in adversarial training. The model simultaneously controls the topics of the essay and the sentiment of each sentence composing the essay. The topic control is achieved using a Topic Graph Attention, which includes a topic knowledge graph in the generation process. Sentiment control is achieved by injecting the sentiment representation both in the encoder and the
decoder. In a different direction, Xie et al. (2022) propose a psychology-guided story generation method that controls storytelling as the protagonist’s psychological state changes. This technique enables multi-attribute control considering the protagonist of the story (Character), their chain of emotions (Emotion), and chain of needs (Need) representing the evolution of the psychological state of the protagonist. The model is an encoder-decoder architecture with the addition of psychology controllers designed to integrate the local and global psychological state into the story context representation.

heading: 5.2 Model Fine-Tuning
text: Model Fine-Tuning can be achieved in many ways. One way is to focus on prefix tuning, i.e. finetuning a model to extract continuous attributespecific vectors that will be prepended to the activations of the pre-trained model to steer text generation. E.g., Qian et al. (2022) fine-tune GPT-2 (Radford et al., 2019) to obtain prefixes, but they use the contrast between prefixes, for example, positive vs negative sentiment, to encourage the desired attribute and discourage the opposite attribute. In this method, only the prefixes are trained and GPT-2 weights are kept frozen.
Similarly, Gu et al. (2022b) fine-tune BERT (Devlin et al., 2019) to obtain prefixes. The idea is to have an Autoencoder structure, i.e. the encoderdecoder reconstructs the input sentence, to map attribute-relevant sentences to latent representations of attributes. At inference time, the model searches the attribute representation in the attribute space and uses it as a prefix for the decoder. In the case of multiple attributes the intersection of attributes is taken as the prefix, instead of contrastive prefixes (Qian et al., 2022). In this setting, the decoder is fixed, while the encoder is fine-tuned to get the attribute representations.
Fang et al. (2022) further explore the usage of Variational Autoencoders to learn a latent representation of control attributes. The idea is to use contrastive learning to separate the latent space into several parts, thus obtaining learnable vectors associated with a control attribute. At inference time, all the vectors associated with the desired attribute are extracted and combined with a Dirichlet distribution to produce a latent variable, which is fed to the decoder.
All three methods allow the control of multiple attributes (sentiment and topic) at the same time.
The last supports control of the tense of the sentences together with the other attributes.

heading: 5.3 Disentanglement
text: Yu et al. (2021) learn an alignment function to transform the control attribute into an aligned attribute representation. The Bayes rule is used to separate attributes encouraging the alignment function to encode different attributes to different representations. The aligned representation is given to a pre-trained LM (PLM) to steer the generation toward the given control attributes. This method enables control of multiple attributes at the same time (sentiment and topic).

heading: 5.4 Modification of Token Distribution
text: Dathathri et al. (2019) propose a Plug and Play Language Model (PPLM) which uses external attribute classifiers to guide text generation without requiring any training of the PLM. The PLM is used to obtain the next token distribution, which is fed to external classifiers, called Attribute Models, to assess whether the token correctly expresses the desired attributes. The internal latent representations of the LM are updated with a backward pass using the gradients of the attribute models to increase the likelihood of the desired attributes. Finally, the next token distribution is recomputed taking into account the updated latent representations. This model allows control of multiple attributes at a time, such as sentiment and topic.
Inspired by this work, Madotto et al. (2020) propose a variation of PPLMs in which the backward pass is executed n times depending on the desired intensity of the control attribute. Furthermore, they add Residual Adapters (Houlsby et al., 2019) on top of each transformer layer to steer the PLM output distribution without changing its parameters.
Goswamy et al. (2020) propose a different variation of PPLMs based on GPT-2, in which a modified loss is considered to take into account the intensity of the controlled sentiment. Furthermore, instead of considering only positive/negative sentiment, control over 8 emotion categories is enabled.
Starting from PPLMs, Gu et al. (2022a) observe that using a controller alone leads to the trade-off problem, i.e. the controller used to modify the token distribution only focuses on how to make the prefix related to the desired attribute without taking into account the original distribution of the LM. In this way, the controller takes over the LM’s control for the next token distribution. In order to alleviate
this problem, they propose a weighted decoding method that adds a regulator module that permits fine-grained adjustment of a bias signal from the controller. At every step, the regulator detects differences between the PLM distribution and the target attribute and it determines whether to suppress or amplify the bias signal. This method is model agnostic and has been evaluated with sentiment, topic, and toxicity attributes.
The last two methods propose sampling procedures that can be applied to any LM. Landsman et al. (2022) propose to modify beam search by reweighing the token candidate likelihoods to control different attributes. Diverse beam search (Vijayakumar et al., 2016) is used to decode k candidates, which are then scored using an attribute model. The obtained scores are used to reweigh the original likelihoods to produce a reweighed candidate distribution that considers both fluency and attribute characteristics. The resulting distribution is used to sample the next token.
Lastly, Kumar et al. (2022) propose a sampling method combining LM log-likelihoods with arbitrary constraints in a single energy function generating samples in a non-autoregressive manner. The idea is to use a PLM without changing its distribution but sampling from it considering different constraints, i.e. control attributes. The constraints are discriminative classifiers trained from scratch or fine-tuned. This method allows multi-attribute control (sentiment and toxicity).

heading: 5.5 Hybrid
text: Hybrid techniques combine two or more Control Implementation techniques. One possibility is to combine Complete Training and Fine-Tuning, for example, designing a model composed of different modules in which some modules are trained from scratch and some are fine-tuned models. In this context, Tian et al. (2022) propose a conversa-
tion model that generates empathetic responses and guides the mood of the conversation in a positive direction while acknowledging the user’s emotion. The idea is to extract the sentiment from the conversation context using a fine-tuned sentiment evaluator and use both the context and the extracted sentiment to steer the generation of the next response by generating a responding strategy that will be used by the Conditional Conversation model to generate the final response. The proposed method enables only single-attribute control (of sentiment).
Another way to enable controllability using a hybrid technique is to combine Fine-Tuning and Modification of Token Distribution. Wang et al. (2022) propose a technique to control Story Generation by fine-tuning an encoder that learns the representation of new special tokens identifying the control attributes, thus allowing the model to properly include this information in the generation process. The next token distribution is obtained by combining the decoder distribution and the attention distribution, which allows the model to copy important information from the specified control attributes. The model allows fine-grained control taking into account the characters of the story with their actions and emotions.
In contrast to Wang et al. (2022) who learn the representation of special tokens during fine-tuning, Liu et al. (2021) propose to modify an LM’s token distribution including two fine-tuned versions of the PLM: an expert, focused on the desired attribute, and an anti-expert, focused on the opposite of the desired attribute. The next token distribution is obtained by subtracting the anti-expert distribution from the expert one and combining the result with the distribution of the frozen PLM to maintain fluency. This method enables the control only of one control attribute at a time and it has been tested on sentiment and toxicity attributes.
Similarly, Krause et al. (2021) propose to con-
trast the desired control attribute and its opposite. Instead of fine-tuning specialised LMs for each attribute, GPT-2 is fine-tuned with control codes to obtain a Class-Conditional LM (CCLM). At each time step, the generation is guided by computing classification probabilities for all possible next tokens via the Bayes rule by normalizing two classconditional distributions: conditioned on the desired attribute and conditioned on the undesired attribute. Like the previous method, it allows the control of one attribute at a time and has been evaluated using sentiment, topic, and toxicity attributes.
Liu et al. (2022) also use a CCLM which is finetuned using an external discriminator to generate texts with the desired attributes, supporting multiattribute control. The token distribution is modified based on a contrastive generator that learns effective representations by bringing together positive samples, i.e. samples with desired attributes, and separating negative samples, i.e. samples without desired attributes. The obtained distribution is combined with the distribution of a PLM to maintain the fluency of the generated text. The generated text is fed to the external discriminator to assess whether it contains the desired attributes or not. The model has been tested on the joint control of sentiment and topic.
Zhang and Song (2022) explore the contrast between desired and undesired attributes proposing a fine-tuned LM incorporating the attribute knowledge of a discriminator, similarly to Liu et al. (2022), to optimize continuous virtual tokens called control-prompts. The learned control-prompts are used as prefixes to steer a fixed conditional LM to generate attribute-specific texts. The LM is finetuned using (i) likelihood training, encouraging the LM to generate tokens with higher probability as scored by the discriminator assessing the desired attribute, and (ii) unlikelihood training, keeping the generated tokens away from lower-probability
candidates.

heading: 6 Comparison of Different Techniques
text: In this Section, we compare the methods from the last section in terms of performance, efficiency, and generality.

heading: 6.1 Performance
text: In the performance comparison below, three quality criteria from the CTG field are used, namely attribute relevance, fluency, and diversity. Attribute relevance (AR) (Yu et al., 2021) assesses the proportion of texts correctly generated with the desired sentiment, i.e. the accuracy of the sentiment attribute measured using an external classifier. Details of the external classifier depends on the evaluation procedure, for more details refer to Appendix A. In some cases, instead of reporting the accuracy, the probability of the text being positive is reported (Pos Prob). Fluency is calculated as the perplexity of an external LM (Pichel Campos et al., 2018), while diversity is measured as the proportion of unique n-grams obtained using the Distinct metric (Dist-n in Table 2) (Li et al., 2016). Since both AR and fluency are calculated using an external component, it is difficult to obtain a fair comparison due to the usage of different models.
We consider the techniques that have been evaluated using the prompts used in the evaluation of PPLM (Dathathri et al., 2019) and the prompts extracted from OpenWebText (Gokaslan and Cohen, 2019), as detailed below.
In Table 2, we compare 6 methods that have been evaluated using the PPLM prompts, i.e. 15 prefixes used to start text generation. Perplexity is calculated using three different models; as regards attribute relevance, all the methods train or fine-tune a different classifier (for details regarding models see Appendix A.1). Performance results
are not directly comparable for these two criteria due to the usage of different models.
Diversity is the only metric for which we can have a fair comparison and here we can see that the technique proposed by Yu et al. (2021) manages to have good variety in the generated texts, while also maintaining good fluency.
Lastly, we compare three techniques evaluated using the OpenWebText (OWT) prompts, composed of neutral, positive, or negative prompts (Table 3). Two different models have been used to compute perplexity, while for the computation of attribute relevance, all the techniques use the same classifier. More details are in Appendix A.2.
In Table 3, we can see that the technique proposed by Landsman et al. (2022) obtains the highest AR and Diversity in both positive and negative target sentiment. This suggests that the proposed technique is able to generate text in the correct sentiment using diverse tokens. The same classifier is used to compute AR for every technique, allowing a fair comparison between them. On the other hand, different LMs are used to calculate Fluency, so it is more difficult to decide whether the differences are due to the model used during evaluation or due to the proposed technique.

heading: 6.2 Generality and parameter efficiency
text: In terms of generality, some of the techniques we have discussed are highly specialised and require many modifications to adapt them to include more or new control attributes. For example, the technique proposed by Xie et al. (2022) is specifically designed to control emotions and needs representing the psychological state of the story’s protagonist. Other techniques require the training or finetuning of specific models for each control attribute (Liu et al., 2021).
In terms of efficiency, we see some techniques that require the storage and usage of multiple LMs (Liu et al., 2021). On the other hand, many techniques are model agnostic, so they can be applied to any PLM allowing reuse of existing models (Landsman et al., 2022 and Dathathri et al., 2019). In Table 4, we compare the studied techniques in terms of the number of trainable parameters. In Model Agnostic techniques, we consider the number of parameters considering the models used in the reference paper. Unfortunately, it is not possible for all techniques to correctly identify the number of parameters. In general, the modification of token
Distribution techniques have fewer trainable parameters than others. The techniques proposed by Gu et al. (2022a) and Landsman et al. (2022) have 0 trainable parameters because they are sampling procedures using a PLM without any training or fine-tuning. More details regarding model parameters in Appendix B.

heading: 7 Future direction and work
text: In this section, we summarise the future direction and work described in the analysed papers. Overall, we can identify two suggested directions: model generalisation and fine-grained control.
Model generalisation. Different works suggest to explore the generalisation of the proposed models to explore their capabilities across domains. This can be achieved by introducing the usage of more controlled attributes, such as writer’s style and dialog acts (Betti et al., 2020, Yu et al., 2021 and Liu et al., 2022), and the usage of more tasks, such as poetry generation, machine translation, and intelligent education agents (Xie et al., 2022 and Fang et al., 2022).
Fine-grained control. Some works also suggest
to explore the capabilities of the proposed methods to support fine-grained control. For example, we can extend the methods to include control attributes in the table-to-text scenario (Zhang and Song, 2022) or explore correlation between different attributes combination to enable fine-grained control (Gu et al., 2022b).

heading: 8 Discussion
text: In this Section, we discuss issues and trends observed in the studied techniques, which suggest possible future directions for the field.
Lack of a standard evaluation procedure. We observe that it is difficult to directly compare the performance of models evaluated on the same dataset using the same metrics, due to the usage of different methods for the metrics’ evaluation. In fact, considering the six methods evaluated with PPLM prompts (Section 6.1), we observe that each method has been evaluated using a different classifier to calculate the AR metric. The usage of different classifiers affects the final result of the metric, thus requiring that every work recomputes all the evaluations to have a fair comparison with previous work. Furthermore, we observe that the papers use different datasets for the evaluation, making the comparison between papers even more difficult.
Lack of combination of different control attribute types. We observe that it is mainly topic and toxicity that are explored in combination with sentiment. While topic is a Content Control attribute, other content control attributes, such as data or set of words, are not explored in combination with sentiment, suggesting that a possible future direction is to investigate the combination of such control attributes. Furthermore, we see that Syntactic Control is not explored in combination with sentiment. Moreover, there are not many combinations of different attributes in the same category. For example, sentiment and toxicity (Polarity Control) are used together to enable multiple control in just two papers (Qian et al., 2022; Gu et al., 2022b)), but not many other attributes are widely explored.

heading: 9 Conclusion
text: We have reported a systematic survey of SentimentControl Text Generation techniques spanning the years 2019–2022. We proposed a categorisation scheme to analyse the studied papers based on the control attributes used and on how the control is
implemented. We compared the papers based on their performance, generality and efficiency. While analysing the selected papers, we observed some issues and trends, such as the lack of a standard evaluation procedure and the lack of combinations between different control attribute types.

heading: Acknowledgements
text: This work was conducted with the financial support of the Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality (d-real) under Grant No. 18/CRT/6224 and the Science Foundation Ireland under Grant Agreement No. 13/RC/2106_P2 at the ADAPT SFI Research Centre at Dublin City University. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

heading: A Performance Comparison of Different Techniques
text: In this Section, we provide further details about the automatic evaluation reported in Section 6.1. Furthermore, we report all the models that have been used in the different techniques to calculate the evaluation metrics explained.
A.1 Comparison using PPLM prompts
Perplexity has been calculated using three different models. Dathathri et al. (2019) and Gu et al. (2022a) used GPT (Radford et al., 2018), Qian et al. (2022), Gu et al. (2022b), and Yu et al. (2021) used GPT-2 Large, Kumar et al. (2022) used while GPT-2 XL. Regarding attribute relevance, all the methods trained or fine-tuned a different classifier. Dathathri et al. (2019) trained a classifier on SST-5 (Socher et al., 2013), while Gu et al. (2022a) trained a classifier on IMDb movie reviews (Maas et al., 2011). Qian et al. (2022) and Gu et al. (2022b) fine-tuned RoBERTa (Liu et al., 2019) and DeBERTa (He et al., 2020), respectively, on the Yelp Review dataset (Zhang et al., 2015). Finally, Yu et al. (2021) fine-tune BERT with IMDb movie reviews dataset, while Kumar et al. (2022) fine-tuned SieBERT (Heitmann et al., 2020) on 15 different polarity datasets.
A.2 Comparison using OWT prompts
The techniques in Table 3 have been evaluated using the OpenWebText prompts, which are prompts randomly selected from OpenWebText dataset. For each selected prompt 25 completions are generated using a base LM. Based on the average sentiment of these completions, each prompt is labeled as neutral, positive, or negative resulting in 3 datasets
of prompts containing respectively 5K, 2,5K, and 2.5K prompts
In order to compute perplexity, Zhang and Song (2022) used GPT-2 Large, and Landsman et al., 2022 and Liu et al., 2021 used GPT-2 XL. While for the computation of attribute relevance, all the techniques used the same classifier, i.e. a DistilBERT (Sanh et al., 2019) sentiment classifier provided by Huggingface and fine-tuned on SST-2 (Socher et al., 2013).

heading: B Parameters Comparison of Different Techniques
text: In Table 5, we show the number of parameters in each studied technique. We report all the components of the techniques with the respective parameters (Column 2), the number of trainable parameters (Column 3), and the total number of parameters (Column 4).
In some of the techniques, we can see a huge difference between the number of trainable parameters and the total number of parameters, for example, (Zhang and Song, 2022) and (Landsman et al., 2022).

heading: 5.175M 350.175M

heading: Future Work
text: In this Section, we discuss issues and trends observed in the studied techniques, which suggest possible future directions for the field. Lack of a standard evaluation procedure. We observe that it is difficult to directly compare the performance of models evaluated on the same dataset using the same metrics, due to the usage of different methods for the metrics’ evaluation. In fact, considering the six methods evaluated with PPLM prompts (Section 6. 1), we observe that each method has been evaluated using a different classifier to calculate the AR metric. The usage of different classifiers affects the final result of the metric, thus requiring that every work recomputes all the evaluations to have a fair comparison with previous work. Furthermore, we observe that the papers use different datasets for the evaluation, making the comparison between papers even more difficult. Lack of combination of different control attribute types. We observe that it is mainly topic and toxicity that are explored in combination with sentiment. While topic is a Content Control attribute, other content control attributes, such as data or set of words, are not explored in combination with sentiment, suggesting that a possible future direction is to investigate the combination of such control attributes. Furthermore, we see that Syntactic Control is not explored in combination with sentiment. Moreover, there are not many combinations of different attributes in the same category. For example, sentiment and toxicity (Polarity Control) are used together to enable multiple control in just two papers (Qian et al. , 2022; Gu et al. , 2022b)), but not many other attributes are widely explored