Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data
Canwen Xu, Daya Guo, Nan Duan, Julian McAuley · 2023
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 highquality 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.Additionally, 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. 1