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.- Anthology ID:
- 2023.emnlp-main.385
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6268–6278
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.385
- DOI:
- 10.18653/v1/2023.emnlp-main.385
- Cite (ACL):
- Canwen Xu, Daya Guo, Nan Duan, and Julian McAuley. 2023. Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6268–6278, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data (Xu et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2023.emnlp-main.385.pdf