@inproceedings{li-etal-2023-autoconv,
title = "{A}uto{C}onv: Automatically Generating Information-seeking Conversations with Large Language Models",
author = "Li, Siheng and
Yang, Cheng and
Yin, Yichun and
Zhu, Xinyu and
Cheng, Zesen and
Shang, Lifeng and
Jiang, Xin and
Liu, Qun and
Yang, Yujiu",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.149",
doi = "10.18653/v1/2023.acl-short.149",
pages = "1751--1762",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models
%A Li, Siheng
%A Yang, Cheng
%A Yin, Yichun
%A Zhu, Xinyu
%A Cheng, Zesen
%A Shang, Lifeng
%A Jiang, Xin
%A Liu, Qun
%A Yang, Yujiu
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F li-etal-2023-autoconv
%X 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.
%R 10.18653/v1/2023.acl-short.149
%U https://aclanthology.org/2023.acl-short.149
%U https://doi.org/10.18653/v1/2023.acl-short.149
%P 1751-1762
Markdown (Informal)
[AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models](https://aclanthology.org/2023.acl-short.149) (Li et al., ACL 2023)
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.