@inproceedings{liu-etal-2024-gprooft,
title = "{GP}roof{T}: A Multi-dimension Multi-round Fact Checking Framework Based on Claim Fact Extraction",
author = "Liu, Jiayu and
Tang, Junhao and
Wang, Hanwen and
Xu, Baixuan and
Shi, Haochen and
Wang, Weiqi and
Song, Yangqiu",
editor = "Schlichtkrull, Michael and
Chen, Yulong and
Whitehouse, Chenxi and
Deng, Zhenyun and
Akhtar, Mubashara and
Aly, Rami and
Guo, Zhijiang and
Christodoulopoulos, Christos and
Cocarascu, Oana and
Mittal, Arpit and
Thorne, James and
Vlachos, Andreas",
booktitle = "Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.fever-1.14",
pages = "118--129",
abstract = "In the information era, the vast proliferation of online content poses significant challenges, particularly concerning the trustworthiness of these digital statements, which can have profound societal implications. Although it is possible to manually annotate and verify the authenticity of such content, the sheer volume and rapid pace of information generation render this approach impractical, both in terms of time and cost. Therefore, it is imperative to develop automated systems capable of validating online claims, ensuring that users can use the wealth of information available on the Internet effectively and reliably. Using primarily ChatGPT and the Google search API, GProofT fact checking framework generates question-answer pairs to systematically extract and verify the facts within claims. Based on the outcomes of these QA pairs, claims are subsequently labeled as Supported, Conflicted Evidence/Cherry-Picking, or Refuted. Shown by extensive experiments, GProofT Retrieval generally performs effectively in fact-checking and makes a substantial contribution to the task. Our code is released on https://github.com/HKUST-KnowComp/GProofT.",
}
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<abstract>In the information era, the vast proliferation of online content poses significant challenges, particularly concerning the trustworthiness of these digital statements, which can have profound societal implications. Although it is possible to manually annotate and verify the authenticity of such content, the sheer volume and rapid pace of information generation render this approach impractical, both in terms of time and cost. Therefore, it is imperative to develop automated systems capable of validating online claims, ensuring that users can use the wealth of information available on the Internet effectively and reliably. Using primarily ChatGPT and the Google search API, GProofT fact checking framework generates question-answer pairs to systematically extract and verify the facts within claims. Based on the outcomes of these QA pairs, claims are subsequently labeled as Supported, Conflicted Evidence/Cherry-Picking, or Refuted. Shown by extensive experiments, GProofT Retrieval generally performs effectively in fact-checking and makes a substantial contribution to the task. Our code is released on https://github.com/HKUST-KnowComp/GProofT.</abstract>
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%0 Conference Proceedings
%T GProofT: A Multi-dimension Multi-round Fact Checking Framework Based on Claim Fact Extraction
%A Liu, Jiayu
%A Tang, Junhao
%A Wang, Hanwen
%A Xu, Baixuan
%A Shi, Haochen
%A Wang, Weiqi
%A Song, Yangqiu
%Y Schlichtkrull, Michael
%Y Chen, Yulong
%Y Whitehouse, Chenxi
%Y Deng, Zhenyun
%Y Akhtar, Mubashara
%Y Aly, Rami
%Y Guo, Zhijiang
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Mittal, Arpit
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F liu-etal-2024-gprooft
%X In the information era, the vast proliferation of online content poses significant challenges, particularly concerning the trustworthiness of these digital statements, which can have profound societal implications. Although it is possible to manually annotate and verify the authenticity of such content, the sheer volume and rapid pace of information generation render this approach impractical, both in terms of time and cost. Therefore, it is imperative to develop automated systems capable of validating online claims, ensuring that users can use the wealth of information available on the Internet effectively and reliably. Using primarily ChatGPT and the Google search API, GProofT fact checking framework generates question-answer pairs to systematically extract and verify the facts within claims. Based on the outcomes of these QA pairs, claims are subsequently labeled as Supported, Conflicted Evidence/Cherry-Picking, or Refuted. Shown by extensive experiments, GProofT Retrieval generally performs effectively in fact-checking and makes a substantial contribution to the task. Our code is released on https://github.com/HKUST-KnowComp/GProofT.
%U https://aclanthology.org/2024.fever-1.14
%P 118-129
Markdown (Informal)
[GProofT: A Multi-dimension Multi-round Fact Checking Framework Based on Claim Fact Extraction](https://aclanthology.org/2024.fever-1.14) (Liu et al., FEVER 2024)
ACL
- Jiayu Liu, Junhao Tang, Hanwen Wang, Baixuan Xu, Haochen Shi, Weiqi Wang, and Yangqiu Song. 2024. GProofT: A Multi-dimension Multi-round Fact Checking Framework Based on Claim Fact Extraction. In Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER), pages 118–129, Miami, Florida, USA. Association for Computational Linguistics.