@inproceedings{rothermel-etal-2024-infact,
title = "{I}n{F}act: A Strong Baseline for Automated Fact-Checking",
author = "Rothermel, Mark and
Braun, Tobias and
Rohrbach, Marcus and
Rohrbach, Anna",
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-1.12",
pages = "108--112",
abstract = "The spread of disinformation poses a global threat to democratic societies, necessitating robust and scalable Automated Fact-Checking (AFC) systems. The AVeriTeC Shared Task Challenge 2024 offers a realistic benchmark for text-based fact-checking methods. This paper presents Information-Retrieving Fact-Checker (InFact), an LLM-based approach that breaks down the task of claim verification into a 6-stage process, including evidence retrieval. When using GPT-4o as the backbone, InFact achieves an AVeriTeC score of 63{\%} on the test set, outperforming all other 20 teams competing in the challenge, and establishing a new strong baseline for future text-only AFC systems. Qualitative analysis of mislabeled instances reveals that InFact often yields a more accurate conclusion than AVeriTeC{'}s human-annotated ground truth.",
}
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%0 Conference Proceedings
%T InFact: A Strong Baseline for Automated Fact-Checking
%A Rothermel, Mark
%A Braun, Tobias
%A Rohrbach, Marcus
%A Rohrbach, Anna
%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 rothermel-etal-2024-infact
%X The spread of disinformation poses a global threat to democratic societies, necessitating robust and scalable Automated Fact-Checking (AFC) systems. The AVeriTeC Shared Task Challenge 2024 offers a realistic benchmark for text-based fact-checking methods. This paper presents Information-Retrieving Fact-Checker (InFact), an LLM-based approach that breaks down the task of claim verification into a 6-stage process, including evidence retrieval. When using GPT-4o as the backbone, InFact achieves an AVeriTeC score of 63% on the test set, outperforming all other 20 teams competing in the challenge, and establishing a new strong baseline for future text-only AFC systems. Qualitative analysis of mislabeled instances reveals that InFact often yields a more accurate conclusion than AVeriTeC’s human-annotated ground truth.
%U https://aclanthology.org/2024.fever-1.12
%P 108-112
Markdown (Informal)
[InFact: A Strong Baseline for Automated Fact-Checking](https://aclanthology.org/2024.fever-1.12) (Rothermel et al., FEVER 2024)
ACL
- Mark Rothermel, Tobias Braun, Marcus Rohrbach, and Anna Rohrbach. 2024. InFact: A Strong Baseline for Automated Fact-Checking. In Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER), pages 108–112, Miami, Florida, USA. Association for Computational Linguistics.