AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models

Siheng Li, Cheng Yang, Yichun Yin, Xinyu Zhu, Zesen Cheng, Lifeng Shang, Xin Jiang, Qun Liu, Yujiu Yang


Abstract
Information-seeking conversation, which aims to help users gather information through conversation, has achieved great progress in recent years. However, the research is still stymied by the scarcity of training data. To alleviate this problem, we propose AutoConv for synthetic conversation generation, which takes advantage of the few-shot learning ability and generation capacity of large language models (LLM). Specifically, we formulate the conversation generation problem as a language modeling task, then finetune an LLM with a few human conversations to capture the characteristics of the information-seeking process and use it for generating synthetic conversations with high quality. Experimental results on two frequently-used datasets verify that AutoConv has substantial improvements over strong baselines and alleviates the dependence on human annotation. In addition, we also provide several analysis studies to promote future research.
Anthology ID:
2023.acl-short.149
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2023
Address:
Toronto, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1751–1762
Language:
URL:
https://aclanthology.org/2023.acl-short.149
DOI:
Bibkey:
Cite (ACL):
Siheng Li, Cheng Yang, Yichun Yin, Xinyu Zhu, Zesen Cheng, Lifeng Shang, Xin Jiang, Qun Liu, and Yujiu Yang. 2023. AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1751–1762, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models (Li et al., ACL 2023)
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PDF:
https://preview.aclanthology.org/paclic-22-ingestion/2023.acl-short.149.pdf