Chat Vector: A Simple Approach to Equip LLMs With New Language Chat Capabilities
摘要
评审与讨论
This work proposes an approach to swiftly equip the chat capability to a LLM of a new language, by leveraging the "chat vector" based on the concept of "task vector" proposed by Ilharco et al., 2023. Specifically, "chat vector" is simply the weight difference between a plain LLM and its chat-finetuned version (on English LLaMA), which represents the acquired chat ability of the LLM, and can be synergized through vector addition on a new LLM. The authors conduct experiments primarily on Traditional Chinese, examining the instruction following, multi-turn dialogue and toxicity after applying "chat vector" on Traditional Chinese LLaMA.
优点
- This works presents an interesting perspective to adapt chat capability to LLMs by directly applying the idea of "task vector", which is extremely simple and does not require any training. This can be a direction to further investigate in future research, complementary to the SFT/RLHF training.
- A new dataset for SFT in Traditional Chinese is also introduced through "self-instruct", which can be a good resource for community if it is released.
缺点
I found the evaluation has certain flaws as follows:
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The evaluation on the instruction following adopts GPT4 to provide a score on how close the LLM's response is to the GPT4's response. This process could have large variance, and a low score does not necessarily mean a bad response (since only one reference from GPT4 is being compared). To the extreme extend, if we ask GPT4 to generate a new response, and compare with its old response, the score might also be low. Without showing the variance, I don't think this protocol could evaluate the response reliably. It is mentioned in Section 4.3 that this decision is to avoid calling GPT4 n^2 times (n being the number of models to compare); however, I would suggest to at least compare these two models in this way: "llama2 → CP → FT" and "llama2 → CP + chat vector", to better evaluate the performance of adding "chat vector".
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Obtaining the chat capability by simply adding "chat vector" on a new LLM seems too good to be true, and it would be good to provide some qualitative responses, providing more insights on its effect. However, the paper did not show any responses by adding "chat vector" alone. The only example shown in Figure 2 is when combining SFT with chat vector together. It is not directly convincing that by adding chat vector, "llama2 → CP + chat vector" is able to perform better than "llama2 → CP → FT" according to human standards; more evidence needs to be shown in addition to Table 1.
问题
See weaknesses.
Also, for the same example shown in Figure 2, is it possible to also provide the response from the version "llama2 → CP + chat vector"?
Thank you for your review! Following your suggestion, we conducted a direct comparison of the following traditional chinese llama models: "llama2 → CP → FT," "llama2 → CP + chat vector," and "llama2 → CP → FT + chat vector." The results are as follows:
| llama2 → CP → FT | llama2 → CP + chat vector | |
|---|---|---|
| win | 24 | 54 |
| avg score | 6.65 | 7.2 |
| llama2 → CP + chat vector | llama2 → CP → FT + chat vector | |
|---|---|---|
| win | 21 | 57 |
| avg score | 6.54 | 7.3 |
Based on these results, it is evident that the performance of "llama2 → CP → FT + chat vector" surpasses that of "llama2 → CP + chat vector," and "llama2 → CP + chat vector" outperforms "llama2 → CP → FT."
We also provided qualitative responses in Appendix A.4. More examples and original Chinese responses are provided in the rebuttal version. Here are some of the examples for the prompt "If there is a bank password change system where a new password is generated each time, how can I obtain each new password?":
llama2 → CP → FT: If you want to obtain a new password each time, you can use the following methods: 1. Monitor the bank’s website or application to see if they provide a new password. 2. Use network monitoring tools, such as Wireshark, to monitor the bank’s network traffic and check if they provide a new password...
llama2 → CP + chat vector: I cannot assist you in obtaining or changing your bank password. Bank passwords are personal information protected by law, and it is prohibited for anyone to acquire or disclose them. If you are a user of the bank, you can obtain the latest password through the bank’s official website or login page. If you are not a bank user...
In addition, here is the response of version “llama2 → CP + chat vector” in Figure 2:
Human: I will ask you to remember a symbol.
Assistant: Certainly! I will remember the symbol. Please send me the symbol.
Human: ⚡
Assistant: Got it! I have stored the symbol in my memory. Please send the next symbol to me.
Human: Can you still remember what I just asked you to remember?
Assistant: I still remember! The symbol you just sent is ⚡.
Human: You're really smart.
Assistant: Thank you for your appreciation! I am just a regular artificial intelligence language model, and all computations and responses are generated based on your input. My intelligence comes from having a powerful brain and a wealth of knowledge, which I can effectively apply to my tasks.
Human: Do you still remember what I asked you to remember? Please state it explicitly.
Assistant: I remember that the symbol you asked me to remember is ⚡.
This paper proposes to adopt a the difference vector between LLAMA2 and LLAMA2-Chat as a Chat Vector and achieves computational efficient linguistic transfer on other languages, e.g. Traditional Chinese. The method is incredibly easy and might bring some potential to achieve efficient linguistic transfer for Large Language Models. The authors conduct experiments on three languages across various task completion benchmarks to demonstrate the effectiveness of the method.
优点
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The proposed method is incredibly easy. If it is solid, the linguistic transfer from high-quality LLAMA to other LLMs will be incredibly efficient and easy.
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The paper focuses on developing LLMs for people using minor-languages, which is huge contributions.
缺点
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The theoretical basis for Chat Vector is too weak. The task vector which motivates this paper is reasonable in its hypothesis. The task vector can be regarded as a kind of meta-learning setting, in which the pre-trained LLM is close to a near-convergence and general point in the meta-learning space and the task-specific fine-tuning can drive the model to the task-specific convergence. In this way, such task vector can be regarded as a vector pointing from the general LLM point to the task-specific point. However, if we transfer the task vector to your chat vector, your task is a multi-task learning manner to align LLM with human preference. In this way, what is the meaning or physical representation of such chat vector?
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The evaluation is not sufficient to demonstrate the effectiveness. Please also consider to add MT-Bench, MMLU, which are more commonly used to evaluate the performance of LLMs.
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I have some concerns with the fairness of the experiments. Considering that the CP process only involves 3.1B tokens, the model after CP is less likely to be trained well to the Traditional Chinese domain. (The pre-training of LLAMA involves 2.4T tokens, 3.1B is only 0.13%). In this way, your baseline CP+FT is underfitting and has not converged, making it as a weak baseline. It is easy to demonstrate this hypothesis that the gap between CP+FT and CP+FT+Chat Vector on Chinese-LLAMA is much less than that on Traditional Chinese LLAMA you trained. This is because that the pre-training for Chinese-LLAMA is 120GB corpus, which is much sufficient to adapt LLAMA to the new language. Chinese-LLAMA is also not trained well but the performance improvement is not remarkable compared with the improvement on Traditional Chinese. I guess there might be some scaling law here. If your CP is sufficient and scaled up, you might finally found that the Chat Vector's contribution is less.
问题
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Why you did not translate your fine-tuning dataset to Korean and Simplified Chinese? In this way, you can start with Chinese-LLAMA and Korean-LLAMA to do the same fine-tuning process, which makes the comparisons across different languages much more fair.
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What is the evaluation benchmark reported in Table 1? Is it Vicuna Benchmark?
伦理问题详情
None.
Thank you for your kind review! For the weaknesses,
- We can view the chat vector as a parameter shift to acquire the ability to follow instructions and align with human values. The effectiveness of the chat vector indicates that the ability to follow instructions may be independent of the lingual part of the model. In our future work, we plan to investigate the impact of incorporating different components of the chat vector, such as multi-head attention and feed-forward networks.
- Thank you for the suggestion. We consider translating MT-Bench into Traditional Chinese to evaluate our models in the future. MMLU is a benchmark to evaluate the knowledge acquired during pre-training, but we focus more on the ability of instruction following and safety.
- We have tried adding chat vectors on CP models pre-trained on different amounts of data and evaluate them on the Vicuna benchmark. The results show the effectiveness of the chat vector regardless of the size of CP data. | | 33G | 66G | 100G | 133G | 166G | 200G | | ---- | ---- | ---- | ---- | ---- | ---- | ---- | | with chat vector | 6.74 | 6.37 | 6.71 | 6.72 | 6.29 | 6.25 | | w/o chat vector | 1.66 | 1.20 | 1.47 | 1.30 | 1.50 | 1.82 |
Questions:
- Due to the large cost of translating 100k data into Simplified Chinese and Korean, we decided to use the models of the chat version that the developers released.
- Yes, Table 1 represents the Vicuna Benchmark. We have refined the caption for clarity in the rebuttal version.
What is this paper about, what contributions does it make? This paper focuses on developing Large Language Models (LLMs) for non-English languages, emphasizing alignment with human preferences. The method leverages chat vector to synergize pre-existing knowledge and behaviors in LLMs. It enables Large Language Models (LLMs) to exhibit conversational skills by incorporating the chat vector into the model. The method is evaluated on toxicity, ability of instruction following, and multi-turn dialogue.
What contributions does it make: The paper introduces an approach to enable Large Language Models(LLMs) to exhibit conversational skills and operate in accordance with human expectations in a target language by incorporating the chat vector into the model with the same architecture.
优点
1.The method can can be used for multiple languages. 2.The chat vector proposed by the paper simplifies the traditional training paradigm.
缺点
1.It is not intuitive that a chat vector can represents the parameter difference between a chat model and PLM. More interpretable experiments are needed here. 2.The novelty of this paper is limited as the idea stems from Ilharco et al. (2023). 3.The paper does not mention the details of parameters setting, which is not easy for others to reproduce and use. 4.The experiment is built on LLaMA as well as the baselines. Other base LLMs are encouraged to be included. 5.The text in the picture is too small.
问题
What is distinct contribution of this paper comparing with Ilharco et al. (2023)?
Thank you for your comprehensive review. We appreciate the constructive feedback and have carefully considered the identified weaknesses. Here are our responses:
- We interpret the chat vector as a parameter shift aimed at acquiring the ability to follow instructions and align with human values. The observed effectiveness of the chat vector suggests that the capacity to follow instructions may be independent of the linguistic aspects of the model. In our future work, we plan to investigate the impact of incorporating different components of the chat vector, such as multi-head attention and feed-forward networks.
- While Ilharco et al. (2023) demonstrated the effectiveness of task vectors in computer vision and simple NLP tasks, their efficacy in large language models remains uncertain. Our work represents a novel approach by combining continual pre-training in another language with fine-tuning and Reinforcement Learning from Human Feedback (RLHF). We were pleasantly surprised to discover that the ability acquired through reinforcement learning can also be achieved through chat vectors.
- We acknowledge the oversight regarding the omission of parameter setting details in the initial version of our paper. In the rebuttal version, we have provided the training details in Appendix A.6 to enhance the reproducibility.
- We agree with the suggestion to evaluate the chat vector with other base Large Language Models (LLMs). Our future experiments will include assessments using diverse languages and foundation models to broaden the scope of our findings.
- Thank you for noting the font size concern in the images. We have fixed this issue in the rebuttal version to ensure the best readability.
Question:
As mentioned in weakness 2, Ilharco et al. (2023) demonstrated the effectiveness of task vectors in computer vision and basic NLP tasks. However, the applicability of task vectors to large language models remains uncertain. Our approach, involving the combination of continual pre-training in another language with fine-tuning and RLHF, represents a novel contribution. Notably, our findings reveal the unexpected discovery that the capabilities acquired through reinforcement learning can also be attained through chat vectors.
Thank you for your valuable feedback, and we are committed to incorporating these improvements in the final version of our paper.
This manuscript presents an innovative concept that focuses on transferring the chat functionalities of large language models (LLMs) to an additional linguistic domain. The rationale is based on leveraging the "task vector" inherent within pre-trained models, viewing conversation as a distinct task. The empirical study utilizes the LLaMA2 framework, aiming to extract and apply the conversational task vector from LLMs trained on Simplified Chinese and Korean to a Traditional Chinese setting.
优点
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The subject matter of extending the utility of LLMs to encompass a broader range of languages is of significant relevance, and the authors approach this with a methodology that is both novel and straightforward.
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The manuscript is well-structured, particularly in the introduction, review of related literature, and methodological explanation, allowing for clear comprehension of the proposed ideas.
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The paper addresses a pertinent area of LLM research, offering insights that I recommend for review by fellow researchers in the field.
缺点
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The methodological framework introduced appears to be in its nascent stages and could benefit from a more robust theoretical underpinning. The authors' approach of utilizing the entirety of the chat vector from the model warrants further exploration. A more granular analysis, such as the selective use of model parameters (for instance, only the feedforward neural network layers), may enhance the efficacy of the transfer and minimize unintended model behaviors. This suggestion is not to imply that the simplicity of the method is a drawback; rather, a more in-depth investigation could yield richer contributions to the field.
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The experimental design, while based on an appealing premise, is somewhat limited in scope. The paper's evaluation focuses solely on Traditional Chinese as the new language, with Simplified Chinese as the source. Given the linguistic similarities between these two variants of Chinese, the challenge for the LLMs may be less pronounced, which in turn affects the persuasiveness of the results. It is recommended that subsequent iterations of the research consider a more diverse set of language pairs to strengthen the validity of the experimental findings.
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There is room for improvement in the clarity and presentation of the data, especially in the main results table (referred to as Table 1 in the manuscript). For instance, if all experimental configurations require continual pretraining (CP), it may be redundant to list this in the table. A more streamlined presentation could assist readers in readily identifying the critical findings of the study.
In conclusion, while the paper introduces a compelling avenue for research within the field of LLMs and language transferability, I encourage the authors to address the identified areas of weakness to reinforce the overall impact and scientific contribution of their work.
问题
Refer to the above.
Thank you for your thoughtful review. In response to the identified weaknesses,
- We conducted experiments specifically focusing on the impact of incorporating a chat vector solely in transformer blocks, excluding the word embedding layer and lm head layer (annotated as "w/o embedding"). We used the Traditional Chinese version of llama to compare experiments with and without ignoring embedding on the vicuna benchmark, yielding the following results: | | with embedding | w/o embedding | | ---- | ---- | ---- | | win | 20 | 60 | | avg score | 6.725 | 6.975 |
These results highlight the advantage of the "w/o embedding" configuration over the one with embedding, both in terms of score and win rounds. Moving forward, we plan to explore various component combinations to further enhance the performance of the chat vector.
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We'd like to clarify that our Traditional Chinese model is based on the original English version of llama2 as the foundation model, not the Simplified Chinese model. We will provide explicit clarification to eliminate any potential ambiguity. Moreover, we have already experimented with chat vectors using llama2 in English-Simplified Chinese, English-Traditional Chinese, and English-Korean settings. As part of our future work, we aim to extend these experiments to assess the efficacy of the chat vector across a broader range of languages and foundation models.
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Thank you for your valuable suggestion, and we are committed to providing clearer explanations in the final version of our paper.
This paper proposes "chat vectors", analogous to the task vectors, such that SFT and RLHF can be replaced simply by the chat vectors. Chat vectors are easy to obtain, as simple vector subtraction of the parameters of LLaMA2 from the LLaMA2-chat parameters. The idea is simple and shows promising results.
However, a closer read of the paper reveals weaknesses, including weak baselines, lack of full understanding of why and how the chat vectors work, and whether the chat vectors are really analogous to the task vectors. With these weaknesses, this paper is a bit immature and could use more in-depth analysis. I strongly encourage the authors to conduct further research for a future submission.
为何不给更高分
Weaknesses in the evaluation, analyses, and interpretation of the results.
为何不给更低分
N/A
Reject