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.

翻译：聊天模型（如ChatGPT）展现出令人瞩目的能力，并已迅速应用于众多领域。然而，这些模型仅能通过受限的API访问，为该领域的新研究与进展设置了障碍。我们提出一种流水线方法，通过利用ChatGPT进行自我对话，自动生成高质量的多轮对话语料库。随后，我们采用参数高效调优技术对开源大语言模型LLaMA进行优化。由此产生的模型命名为Baize，在多轮对话中表现出良好性能，并设有防护机制以最大程度降低潜在风险。