Paper: Tiny-StyleWizard: Unleashing the Potential of Small Language Models in Complex Style Transfer (ICLR-2024)

Reviewer comment (Writing):
The paper claims to study "the potential of small language models in complex style transfer", but it did not present a clear definition of "small language model" or "complex style transfer". It is unclear how small the model needs to be to be considered a "small LM", and it is also unclear which transfers can be considered "complex". The authors need to provide a concrete definition for both terms (such as a clear size boundary for "small LM" and a clear linguistic definition of "complex style transfer").

Author response:
* The small language models we refer to are those with fewer than 100 million parameters, such as T5-tiny (31M) and T5-small (60M). As can be seen from Figure 5 in the paper, both of these small language models outperform the existing best models in terms of style transfer accuracy and semantic preservation.
* The complex text style transfer we claim originates from the BTTS paper[1]. In that work, they provide a preliminary definition of complex text style, which refers to styles that are difficult for non-experts to discern and categorize. Such styles might include personality, domain-specific jargon, or other highly specialized terminology. The complexity of these styles makes it challenging to use crowdsourcing for labeling, as only experts in the relevant fields can accurately differentiate between styles. Therefore, the challenge in complex text style transfer is to develop effective models capable of capturing and transferring these nuanced styles without relying heavily on labeled data.

Reviewer comment (Novelty):
The paper currently uses both "small language model" and "complex style transfer" vaguely, which makes it hard to accurately assess the significance of the contribution, as there already exist many parallel corpuses of style transfer that could be considered "complex", such as formality transfer (GYAFC[1]) or composition of multiple fine-grained text style transfer (StylePTB[2]).

Author response:
* The small language models we refer to are those with fewer than 100 million parameters, such as T5-tiny (31M) and T5-small (60M). As can be seen from Figure 5 in the paper, both of these small language models outperform the existing best models in terms of style transfer accuracy and semantic preservation.
* The complex text style transfer we claim originates from the BTTS paper[1]. In that work, they provide a preliminary definition of complex text style, which refers to styles that are difficult for non-experts to discern and categorize. Such styles might include personality, domain-specific jargon, or other highly specialized terminology. The complexity of these styles makes it challenging to use crowdsourcing for labeling, as only experts in the relevant fields can accurately differentiate between styles. Therefore, the challenge in complex text style transfer is to develop effective models capable of capturing and transferring these nuanced styles without relying heavily on labeled data.

Reviewer comment (Reproducibility):
There are no details on how the TinyStyleWizard models are fine-tuned from their pre-trained state.

Author response:
* Details on how the models were fine-tuned: we fine-tuned the pretrained language models (BART, T5, LongLM etc.) with the huggingface coding framework on single A100 GPU. The initial learning rate is 2e-5. The batch size is 4.

Reviewer comment (Writing):
The in-text citation style seems to be wrong, i.e., there are no parentheses around each in-text citation, which makes the text much harder to read, especially in the introduction and related work sections where there are a lot of citations.

Author response:
* We will correct the citation issues in subsequent versions;

Reviewer comment (Presentation):
The ordering of the tables is quite strange. Table 3 is referred to in later context than Tables 4, 5, and 6. Table 6 appears in the paper above Tables 4 and 5.

Author response:
* We will also modify the layout of the tables according to your suggestions.

Reviewer comment (Presentation):
For Table 1, it is unclear what the takeaway message should be. It is unclear whether it is used to show the quality of the collected corpus or to show that the metric is better. In either case, it is hard to draw conclusions from the bolded numbers in the table.

Author response:
* Reading Table 1, the main takeaways are two-fold: First, using a few-shot classifier based on ChatGPT to calculate the accuracy of style transfer is effective, and it performs better than the model fine-tuned on the training set using DeBERTa. Second, using LLM-score to assess the degree of semantic preservation in style transfer is more reliable than other metrics. Our reasoning is that ChatGPT, trained on massive corpora and reinforced through feedback alignment, naturally preserves the semantics of the original text in its stylized output. A good assessment metric should, therefore, yield a high similarity when evaluating the original text against the text generated by ChatGPT.

Reviewer comment (Experiments):
For Tables 4, 5, and 6, it is unclear why the baselines are different in these settings.

Author response:
* Regarding the differences in baselines: Table 4 shows the experimental results on the Genshin dataset, while Table 5 presents the results on the Shakespeare dataset. The former involves shorter text lengths (under 30 words), and the latter longer text lengths (average length of about 70 words). Both represent complex styles and have some foundational work. We established the baselines based on their respective foundational works.

Reviewer comment (Presentation):
For Table 3, it is unclear which TinyWizard model is "ours" (i.e., which row from Table 4 corresponds to "ours" in Table 3). The best numbers from Table 3 should also be bolded, since the best numbers from all other tables are bolded.

Author response:
* In manual evaluations, we used the fine-tuned BART-large model. We will bold the performance of the best models in all tables.

Reviewer comment (Theory):
It is unclear why "diversity" (sec 3.5.4) is desired in style transfer tasks. If the style is precisely defined and semantic preservation is perfect, then all results should be similar, and a "diverse" result would either mean poor style accuracy in some transfers or poor semantic preservation in some transfers.

Author response:
* As for diversity you mentioned, our game designers hope to express similar semantics within the same style using diverse wordings. According to their rich experience, players do not appreciate encountering repetitive and identical text throughout the game, as it can lead to aesthetic fatigue and diminish their gaming experience. Moreover, we hope that the stylized content viewed by different players will also vary in terms of wrod choice. Therefore, we believe that this metric is extremely important for the gaming business. Thus, we take this diversity metric into accout as well as the conventional style transfer accuracy and semantic preservation.

Reviewer comment (Writing):
Multiple instances of improper grammar (ex. Sentence 2 of section 2 “...it generates”) and unusual wording. Difficult to understand at times with grammar issues. Examples include “unleash the capability of these models” and “Small language models specialized for the magician capabilities of style transfer.” There is also a 3.5.3 title typo “How small can language models take effects,” and the first word in this paragraph should be capitalized.

Author response:
- We will revise the terms that are difficult to understand. Some modifications are listed as follows:

  - unleash the capability of these models -> fully utilize the potential of these models
  - Small language models specialized for the magician capabilities of style transfer -> Small language models specialized for style transfer

Reviewer comment (Reproducibility):
Details are lacking on the ChatGPT-based evaluation metric—there is no description of how this actually works. This is especially important as the authors highlight the proposed metric as one of their 3 novel contributions. In Table 1, ChatGPT is reported but there is no corresponding description of how it is used. The same table also lists ‘fine-tuned’ but does not indicate what is fine-tuned.

Author response:
- We will present the prompts we used and the related results here. We used ChatGPT as a few-shot classifier for categorizing text styles. The prompt we used is shown beneath. [Prompt text here]. The term 'fine-tuned model' refers to the model we obtained by fine-tuning DeBERTa on the training set containing a variety of different style corpora. In our work, the training dataset is the training part of genshin, Shakespeare, LuXun and JinYong. 

  The prompt used for evaluating the style transfer accuracy:

  ```shell
  ${CHARACTER_PROFILE}
  
  You are an excellent style classifier, please determine whether the style presented in the input sentence is consistent with ${YOUR_CHARACTER}'s speaking style. Output either `yes` (consistent) or `no` (inconsistent). 
  
  ${DEMO}
  
  input sentence: ${text}
  label: 
  ```

Reviewer comment (Presentation):
Table 1 can be moved to a later page, closer to when it’s referenced.

Author response:
- We will adjust the layout of the figures and references in the paper based on your suggestions.

Reviewer comment (Evaluation):
In the human evaluations (section 3.4) there is a question regarding whether the evaluators are conflating strong fluency with style matching. Especially for style associated with a particular character, it may be hard to judge which is best if the evaluators aren’t familiar with the characters. This may be an explanation for why ChatGPT performs well.

Author response:
- We invited 10 designers from the gaming industry to evaluate the texts generated by different models. These designers have played multiple open-world games, including 'Genshin Impact.' They are capable of discerning and judging the quality of the stylized texts generated by the models.

Reviewer comment (Experiments):
Question about why the same baselines are not used in Tables 4 and 5.

Author response:
- Regarding the differences in baselines: Table 4 shows the experimental results on the Genshin dataset, while Table 5 presents the results on the Shakespeare dataset. The former has shorter text lengths (under 30 words), and the latter has longer text lengths (average length of about 70 words). Both represent complex styles and have some preliminary work. We set up the baselines based on their respective preliminary works.

Reviewer comment (Novelty):
While the end-to-end system is an interesting use case, I'm not sure this is particularly novel overall. We know that LLMs can create small, high quality datasets, and that those datasets can be used to train smaller models.

Author response:
Our research not only demonstrates that large language models can generate high-quality datasets for training smaller models with comparable performance in complex style transfer tasks, but also investigates the minimal size of language models required to effectively execute complex style transfer. Furthermore, it explores how different dimensions of complex style—specifically, the accuracy of style transfer and the degree of semantic preservation—place varying demands on the size of the language model.

Reviewer comment (Novelty):
Using a different model's embedding space to calculate BERT-score doesn't seem significant enough to include as a contribution.

Author response:
The calculation method of LLM-score is distinct from that of BERT-score. LLM-score assesses the similarity between model-generated text and reference text at the level of the whole sentence (cosine similarity). In contrast, BERT-score requires calculating the precision and recall at the word/token level for the generated text in comparison to the reference text. We believe that complex text styles are manifested across entire sentences, rather than being uniformly distributed across each word.

Reviewer comment (Writing):
Nit: use \citep vs \citet for clarity (https://www.reddit.com/r/LaTeX/comments/5g9kn1/whats_the_difference_between_cite_citep_and_citep/)

Author response:
Thank you for pointing out the issue with inappropriate citation. We will address this in subsequent versions of our paper.

Reviewer comment (Novelty):
"...challenge the conventional wisdom that balancing style transfer accuracy and semantic preservation is difficult" this seems like a strong claim. In my mind, this tradeoff has not been an issue for LLM-based style transfer methods.

Author response:
This traditional perspective has been discussed in the previous paper[1]. At that time, large language models, such as ChatGPT, had not yet achieved remarkable success in various natural language processing tasks. Following the emergence of ChatGPT, our view, similar to yours, is that introducing high-quality parallel corpora generated by large language models can break this trade-off. Therefore, we conducted this study. The experimental results in our paper demonstrate the validity of our idea.

Reviewer comment (Reproducibility):
Do you have the prompts that you used for the style transfer accuracy classifiers?

Author response:
Our prompt for style transfer accuracy classifier:

  ```shell
  ${CHARACTER_PROFILE}
  
  You are an excellent style classifier, please determine whether the style presented in the input sentence is consistent with ${YOUR_CHARACTER}'s speaking style. Output either `yes` (consistent) or `no` (inconsistent). 
  
  ${DEMO}
  
  input sentence: ${text}
  label: 
  ```

Reviewer comment (Experiments):
Also for the style transfer itself (in Table 6)?

Author response:
Our prompt for style transfer in Table 6:

  The prompt to generate parallel corpus for LuXun-style transfer is shown beneath. The initial parallel sample(s) used for demonstration is obtained by zero-shot prompting. The prompt to generate parallel corpus for JinYong-style transfer is similar. 

  ```shell
  给定一段童话文本，在保持原文语义不变的情况下，将其转换为带有鲁迅风格的文本。
  
  ${DEMO}
  
  童话风格的文本：${text}
  鲁迅风格的文本：
  
  Given a piece of fairy tale text, it is transformed into a text with the style of Lu Xun while maintaining the original meaning.
  
  ${DEMO}
  
  Fairy-tale style text: ${text}
  LuXun-style text:
  ```