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Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages (2310.04799v3)

Published 7 Oct 2023 in cs.CL

Abstract: Recently, the development of open-source LLMs has advanced rapidly. Nevertheless, due to data constraints, the capabilities of most open-source LLMs are primarily focused on English. To address this issue, we introduce the concept of $\textit{chat vector}$ to equip pre-trained LLMs with instruction following and human value alignment via simple model arithmetic. The chat vector is derived by subtracting the weights of a pre-trained base model (e.g. LLaMA2) from those of its corresponding chat model (e.g. LLaMA2-chat). By simply adding the chat vector to a continual pre-trained model's weights, we can endow the model with chat capabilities in new languages without the need for further training. Our empirical studies demonstrate the superior efficacy of the chat vector from three different aspects: instruction following, toxicity mitigation, and multi-turn dialogue. Moreover, to showcase the adaptability of our approach, we extend our experiments to encompass various languages, base models, and chat vectors. The results underscore the chat vector's simplicity, effectiveness, and wide applicability, making it a compelling solution for efficiently enabling conversational capabilities in pre-trained LLMs. Our code is available at https://github.com/aqweteddy/ChatVector.

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References (39)
  1. A general theoretical paradigm to understand learning from human preferences.
  2. Language models are few-shot learners. arXiv preprint arXiv: 2005.14165.
  3. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.
  4. Deep reinforcement learning from human preferences. Advances in neural information processing systems, 30.
  5. Efficient and effective text encoding for chinese llama and alpaca. arXiv preprint arXiv:2304.08177.
  6. Elastic weight removal for faithful and abstractive dialogue generation. arXiv preprint arXiv: 2303.17574.
  7. Kto: Model alignment as prospect theoretic optimization.
  8. Scaling laws for reward model overoptimization. International Conference on Machine Learning.
  9. RealToxicityPrompts: Evaluating neural toxic degeneration in language models. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3356–3369, Online. Association for Computational Linguistics.
  10. Lora: Low-rank adaptation of large language models.
  11. Reward learning from human preferences and demonstrations in atari. Advances in neural information processing systems, 31.
  12. Editing models with task arithmetic. In The Eleventh International Conference on Learning Representations.
  13. Camels in a changing climate: Enhancing lm adaptation with tulu 2.
  14. Mistral 7b. arXiv preprint arXiv: 2310.06825.
  15. CTRL - A Conditional Transformer Language Model for Controllable Generation. arXiv preprint arXiv:1909.05858.
  16. L. Junbum. 2023. llama-2-ko-7b (revision 4a9993e).
  17. Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning. Neural Information Processing Systems.
  18. Michael S Matena and Colin A Raffel. 2022. Merging models with fisher-weighted averaging. Advances in Neural Information Processing Systems, 35:17703–17716.
  19. Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv: 2112.09332.
  20. OpenAI. 2023. Gpt-4 technical report.
  21. Task arithmetic in the tangent space: Improved editing of pre-trained models. arXiv preprint arXiv: 2305.12827.
  22. Training language models to follow instructions with human feedback.
  23. Language models are unsupervised multitask learners.
  24. Direct preference optimization: Your language model is secretly a reward model. NEURIPS.
  25. Rewarded soups: towards pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards. arXiv preprint arXiv: 2306.04488.
  26. Elyza-japanese-llama-2-7b.
  27. Proximal policy optimization algorithms. arXiv preprint arXiv: 1707.06347.
  28. Safety assessment of chinese large language models. arXiv preprint arXiv:2304.10436.
  29. Xwin-LM Team. 2023. Xwin-lm.
  30. Llama: Open and efficient foundation language models.
  31. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv: 2307.09288.
  32. Self-instruct: Aligning language model with self generated instructions.
  33. Bloom: A 176b-parameter open-access multilingual language model.
  34. Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. International Conference on Machine Learning.
  35. Robust fine-tuning of zero-shot models. Computer Vision and Pattern Recognition.
  36. Resolving interference when merging models. arXiv preprint arXiv: 2306.01708.
  37. YuLan-Team. 2023. Yulan-chat: An open-source bilingual chatbot. https://github.com/RUC-GSAI/YuLan-Chat.
  38. Composing parameter-efficient modules with arithmetic operations. arXiv preprint arXiv: 2306.14870.
  39. Fine-tuning language models from human preferences. arXiv preprint arXiv: 1909.08593.
Citations (7)
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Summary

  • The paper's main contribution is introducing the chat vector framework that adapts LLMs to new languages using arithmetic operations on model weights.
  • It demonstrates improved instruction adherence, toxicity mitigation, and multi-turn dialogue performance compared to traditional RLHF and SFT methods.
  • The approach achieves computational efficiency, paving the way for parameter-efficient adaptation strategies in multilingual NLP tasks.

An Analysis of "Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages"

The paper entitled "Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages" presents a novel methodology for adapting LLMs to new linguistic contexts with efficiency and precision. The authors introduce the concept of a "chat vector," derived from the arithmetic operation of subtracting the weights of a pre-trained model from those of its chat-enhanced version. This approach promises to equip LLMs with instruction-following capabilities and alignment with human preferences across varied languages without extensive retraining.

Key Contributions

The primary contribution of this work lies in the introduction and implementation of the chat vector framework. By applying a chat vector to a continually pre-trained model's weights, the paper reveals notable enhancement in instruction adherence, toxicity mitigation, and handling multi-turn dialogue in non-English languages. This is evidenced by evaluations conducted across several benchmarks, such as the Vicuna Benchmark, SAFETYPROMPTS, and REALTOXICITYPROMPTS, with translations facilitated by GPT-4 where necessary. These evaluations demonstrate the effectiveness of integrating chat vectors into LLMs trained for new linguistic contexts, marking a significant step forward in the efficient adaptation of LLMs across languages.

Numerical Results and Contradictory Claims

The empirical results are significant, showing superior performance through the chat vector approach compared to traditional methods such as continual pre-training followed by supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF). Notably, the paper presents a bold claim that the integration of chat vectors, coupled with continual pre-training, is computationally more efficient than reimplementing RLHF for target languages. This bold assertion challenges the conventional reliance on RLHF, highlighting potential efficiencies in computational resources and memory when leveraging arithmetic operations on model weights.

Implications and Future Developments

The implications of this research are profound, both theoretically and practically. Theoretically, it deepens our understanding of the structure of learning weights within the parameter space of LLMs, potentially leading to more refined models with enhanced transfer learning capabilities. Practically, the chat vector approach provides a viable and efficient pathway for the deployment of LLMs in multilingual contexts, benefiting from reduced computational overheads and the effective alignment of models with human conversational norms without the need for extensive additional training.

Speculating on future developments in AI, this framework could stimulate a new wave of research focused on parameter-efficient adaptation strategies, particularly for languages with limited annotated resources. The findings also invite future exploration into the optimization of chat vector magnitude and the careful tuning of these vectors across different linguistic domains and tasks.

Conclusion

In sum, the "Chat Vector" paper presents a methodologically sound and computationally efficient approach to equipping LLMs with valuable conversational capabilities in new languages. By harnessing the power of simple model arithmetic, this research potentially alters the landscape of multilingual LLM deployment, offering a sophisticated yet straightforward tool for aligning models with nuanced linguistic and cultural dynamics. This paper could act as a catalyst for broader explorations into the integration of human preference alignment in machine learning models, ultimately driving advancements in the field of natural language processing.

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