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Chat models, such as ChatGPT, have shown impressive capabilities and havebeen rapidly adopted across numerous domains. However, these models are onlyaccessible through a restricted API, creating barriers for new research andprogress in the field. We propose a pipeline that can automatically generate ahigh-quality multi-turn chat corpus by leveraging ChatGPT to engage in aconversation with itself. Subsequently, we employ parameter-efficient tuning toenhance LLaMA, an open-source large language model. The resulting model, namedBaize, demonstrates good performance in multi-turn dialogues with guardrailsthat minimize potential risks. Furthermore, we propose a new technique calledSelf-Distill with Feedback, to further improve the performance of the Baizemodels with feedback from ChatGPT. The Baize models and data are released forresearch purposes only at https://github.com/project-baize/baize-chatbot. Anonline demo is also available athttps://huggingface.co/spaces/project-baize/chat-with-baize.

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