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Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data

Published 3 Apr 2023 in cs.CL and cs.AI | (2304.01196v4)

Abstract: Chat models, such as ChatGPT, have shown impressive capabilities and have been rapidly adopted across numerous domains. However, these models are only accessible through a restricted API, creating barriers for new research and progress in the field. We propose a pipeline that can automatically generate a high-quality multi-turn chat corpus by leveraging ChatGPT to engage in a conversation with itself. Subsequently, we employ parameter-efficient tuning to enhance LLaMA, an open-source LLM. The resulting model, named Baize, demonstrates good performance in multi-turn dialogues with guardrails that minimize potential risks. Furthermore, we propose a new technique called Self-Distill with Feedback, to further improve the performance of the Baize models with feedback from ChatGPT. The Baize models and data are released for research purposes only at https://github.com/project-baize/baize-chatbot. An online demo is also available at https://huggingface.co/spaces/project-baize/chat-with-baize.

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References (29)
  1. Towards a human-like open-domain chatbot. arXiv preprint arXiv:2001.09977.
  2. Asma Ben Abacha and Dina Demner-Fushman. 2019. A question-entailment approach to question answering. BMC bioinformatics, 20(1):1–23.
  3. Vicuna: An open-source chatbot impressing gpt-4 with 90% chatgpt quality. https://vicuna.lmsys.org/.
  4. Think you have solved question answering? try arc, the ai2 reasoning challenge. arXiv preprint arXiv:1803.05457.
  5. A framework for few-shot language model evaluation.
  6. Parameter-efficient transfer learning with diff pruning. In ACL-IJCNLP, pages 4884–4896. Association for Computational Linguistics.
  7. Measuring massive multitask language understanding. In ICLR. OpenReview.net.
  8. The curious case of neural text degeneration. In ICLR. OpenReview.net.
  9. Parameter-efficient transfer learning for NLP. In ICML, volume 97 of Proceedings of Machine Learning Research, pages 2790–2799. PMLR.
  10. Lora: Low-rank adaptation of large language models. In ICLR. OpenReview.net.
  11. Using chatgpt to evaluate cancer myths and misconceptions: artificial intelligence and cancer information. JNCI Cancer Spectrum, 7(2):pkad015.
  12. Xiang Lisa Li and Percy Liang. 2021. Prefix-tuning: Optimizing continuous prompts for generation. In ACL-IJCNLP, pages 4582–4597. Association for Computational Linguistics.
  13. Controllable dialogue simulation with in-context learning. arXiv preprint arXiv:2210.04185.
  14. Truthfulqa: Measuring how models mimic human falsehoods. In ACL, pages 3214–3252. Association for Computational Linguistics.
  15. Gpt understands, too. arXiv preprint arXiv:2103.10385.
  16. OpenAI. 2023a. Chatgpt: Optimizing language models for dialogue.
  17. OpenAI. 2023b. Gpt-4 technical report.
  18. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9.
  19. Multitask prompted training enables zero-shot task generalization. In ICLR. OpenReview.net.
  20. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
  21. Stanford alpaca: An instruction-following llama model. https://github.com/tatsu-lab/stanford_alpaca.
  22. Lamda: Language models for dialog applications. arXiv preprint arXiv:2201.08239.
  23. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
  24. Self-instruct: Aligning language model with self generated instructions. arXiv preprint arXiv:2212.10560.
  25. Finetuned language models are zero-shot learners. In ICLR. OpenReview.net.
  26. Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models. In ACL, pages 1–9. Association for Computational Linguistics.
  27. Hellaswag: Can a machine really finish your sentence? In ACL, pages 4791–4800. Association for Computational Linguistics.
  28. Dialogpt: Large-scale generative pre-training for conversational response generation. arXiv preprint arXiv:1911.00536.
  29. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593.

Summary

  • The paper presents a novel approach by using self-chat data to generate multi-turn dialogues for fine-tuning the LLaMA model.
  • The paper introduces Self-Distillation with Feedback (SDF) with LoRA, enabling efficient model tuning with reduced computational requirements.
  • The paper demonstrates that releasing Baize and its dataset promotes open research, advancing NLP innovation in areas like healthcare and coding.

Overview of "Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data"

Introduction

The paper presents Baize, an open-source chat model designed to enable easier access and development within the NLP community. The restriction on existing chat models accessible only through limited APIs creates a bottleneck for research progress. Baize addresses this by generating a multi-turn chat corpus using ChatGPT's self-dialogue, then employing parameter-efficient tuning methods to enhance the LLaMA model, culminating in a capable alternative to proprietary chat systems.

Methodology

The methodology primarily focuses on two innovative stages: data collection and model training.

  1. Data Collection through Self-Chat:
    • A novel pipeline is proposed where ChatGPT engages in self-dialogue to generate multi-turn chat data. This process involves using a specific template to simulate user and AI interactions based on a seed dataset from platforms like Quora and Stack Overflow.
    • The pipeline allows for specialization by sampling domain-specific seeds, demonstrated in creating a healthcare-focused Baize model.
  2. Parameter-Efficient Tuning:
    • Baize leverages Low-Rank Adaptation (LoRA) to fine-tune the LLaMA model effectively. LoRA reduces the computational requirements by updating only low-rank matrices, making the training process viable on limited hardware resources.
    • The paper introduces "Self-Distillation with Feedback" (SDF), a refinement method using ChatGPT's feedback to further enhance Baize's performance without requiring extensive computational load, an alternative to traditional Reinforcement Learning with Human Feedback.

Experimental Results

Baize is evaluated against existing models like Alpaca and Vicuna, highlighting its competitive performance. Notable results indicate that Baize v2's performance aligns closely with models such as Vicuna-13B, demonstrating its capability as a resource-efficient alternative.

  • The model's efficacy is validated using GPT-4 scoring and evaluated on standard tasks via the LM Evaluation Harness.
  • Comparisons show Baize's proficiency across various domains, such as coding and healthcare, by employing different specialized datasets.

Implications and Future Directions

The release of Baize and its dataset under research-friendly licenses fosters the development of open-source chat applications. The parameter-efficient model training and public availability encourage wider participation and innovation in NLP research.

Future work could explore enhancing the diversity and quality of self-chat data, further improving Baize's capabilities. The paper posits SDF as a potent tool that may extend beyond ChatGPT to human feedback scenarios, potentially leading to further refinements in AI LLMs.

Conclusion

This paper makes significant strides in democratizing chat model research through an accessible, efficient, and adaptable approach. By utilizing self-dialogue data generation and parameter-efficient tuning, Baize emerges as a valuable resource for advanced research and potential application across diverse domains. The methodologies promise continued advancement in NLP capabilities, enhancing both theoretical exploration and practical deployment.

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