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

• URL: http://arxiv.org/abs/2304.01196v4
• Date: Sat, 2 Dec 2023 21:05:22 GMT
• Title: Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data
• Authors: Canwen Xu and Daya Guo and Nan Duan and Julian McAuley
• Abstract summary: Chat models, such as ChatGPT, have shown impressive capabilities and have been rapidly adopted across numerous domains. We propose a pipeline that can automatically generate a high-quality multi-turn chat corpus by leveraging ChatGPT. We employ parameter-efficient tuning to enhance LLaMA, an open-source large language model.
• Score: 101.63682141248069
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 large language model. 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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