Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data
Submission Type: Regular Long Paper
Submission Track: Dialogue and Interactive Systems
Submission Track 2: NLP Applications
Keywords: large language model, knowledge disillation, data generation, chatbot, chat model, text generation
TL;DR: We propose Baize, an open-source chatbot efficiently trained on self-chat data and then optimized by self-distillation with feedback.
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. Additionally, we propose a new technique called Self-Distill with Feedback, to further improve the performance of the Baize models with feedback from ChatGPT.
Submission Number: 130
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