@inproceedings{chengwei-etal-2024-ji,
title = "基于大模型的交互式谎言识别:数据和模型(Unveiling Lies: Enhancing Large Language Models for Real-World Lie Detection in Interactive Dialogues)",
author = "Ji, Chengwei and
Wang, Siyuan and
Li, Taishan and
Mou, Xinyi and
Zhao, Limin and
Xue, Lanqing and
Ying, Zhenzhe and
Wang, Weiqiang and
Huang, Xuanjing and
Wei, Zhongyu",
editor = "Maosong, Sun and
Jiye, Liang and
Xianpei, Han and
Zhiyuan, Liu and
Yulan, He",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-1.68/",
pages = "870--882",
language = "zho",
abstract = "``面向对话交互过程的谎言识别技术在不同的应用场景有广泛的应用需求。现有的鉴谎技术往往在整体的对话级别上给出最终决策,而缺乏对细粒度谎言特征和线索的逻辑分析,难以满足场景中对于可解释性的需求。本文提出了谎言指征和语义不一致线索的概念,用于帮助识别对话中的谎言,提升鉴谎方法的可解释性。文章同时提出一个谎言识别框架,用于训练谎言识别大语言模型(LD-LLM)。它利用细粒度的谎言指征并且发现对话中是否存在语义不一致线索,以实现更可靠的谎言识别。文章在真实交互场景中构建了两个谎言识别数据集FinLIE和IDLIE,分别关注金融风控场景和身份识别场景。实验结果表明,基于这两个数据集创建的指令数据集微调得到的LD-LLM,在基于真实交互的谎言识别上达到了最先进的水平。''"
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<abstract>“面向对话交互过程的谎言识别技术在不同的应用场景有广泛的应用需求。现有的鉴谎技术往往在整体的对话级别上给出最终决策,而缺乏对细粒度谎言特征和线索的逻辑分析,难以满足场景中对于可解释性的需求。本文提出了谎言指征和语义不一致线索的概念,用于帮助识别对话中的谎言,提升鉴谎方法的可解释性。文章同时提出一个谎言识别框架,用于训练谎言识别大语言模型(LD-LLM)。它利用细粒度的谎言指征并且发现对话中是否存在语义不一致线索,以实现更可靠的谎言识别。文章在真实交互场景中构建了两个谎言识别数据集FinLIE和IDLIE,分别关注金融风控场景和身份识别场景。实验结果表明,基于这两个数据集创建的指令数据集微调得到的LD-LLM,在基于真实交互的谎言识别上达到了最先进的水平。”</abstract>
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%0 Conference Proceedings
%T 基于大模型的交互式谎言识别:数据和模型(Unveiling Lies: Enhancing Large Language Models for Real-World Lie Detection in Interactive Dialogues)
%A Ji, Chengwei
%A Wang, Siyuan
%A Li, Taishan
%A Mou, Xinyi
%A Zhao, Limin
%A Xue, Lanqing
%A Ying, Zhenzhe
%A Wang, Weiqiang
%A Huang, Xuanjing
%A Wei, Zhongyu
%Y Maosong, Sun
%Y Jiye, Liang
%Y Xianpei, Han
%Y Zhiyuan, Liu
%Y Yulan, He
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G zho
%F chengwei-etal-2024-ji
%X “面向对话交互过程的谎言识别技术在不同的应用场景有广泛的应用需求。现有的鉴谎技术往往在整体的对话级别上给出最终决策,而缺乏对细粒度谎言特征和线索的逻辑分析,难以满足场景中对于可解释性的需求。本文提出了谎言指征和语义不一致线索的概念,用于帮助识别对话中的谎言,提升鉴谎方法的可解释性。文章同时提出一个谎言识别框架,用于训练谎言识别大语言模型(LD-LLM)。它利用细粒度的谎言指征并且发现对话中是否存在语义不一致线索,以实现更可靠的谎言识别。文章在真实交互场景中构建了两个谎言识别数据集FinLIE和IDLIE,分别关注金融风控场景和身份识别场景。实验结果表明,基于这两个数据集创建的指令数据集微调得到的LD-LLM,在基于真实交互的谎言识别上达到了最先进的水平。”
%U https://aclanthology.org/2024.ccl-1.68/
%P 870-882
Markdown (Informal)
[基于大模型的交互式谎言识别:数据和模型(Unveiling Lies: Enhancing Large Language Models for Real-World Lie Detection in Interactive Dialogues)](https://aclanthology.org/2024.ccl-1.68/) (Ji et al., CCL 2024)
ACL
- Chengwei Ji, Siyuan Wang, Taishan Li, Xinyi Mou, Limin Zhao, Lanqing Xue, Zhenzhe Ying, Weiqiang Wang, Xuanjing Huang, and Zhongyu Wei. 2024. 基于大模型的交互式谎言识别:数据和模型(Unveiling Lies: Enhancing Large Language Models for Real-World Lie Detection in Interactive Dialogues). In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference), pages 870–882, Taiyuan, China. Chinese Information Processing Society of China.