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

**Authors:** Canwen Xu, Daya Guo, Nan Duan, Julian McAuley  
**arXiv:** [2304.01196](https://arxiv.org/abs/2304.01196)  
**Date:** 2023-04-03  
**Summary page:** https://www.summarizepaper.com/fr/paper/2304.01196v1/

## 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.