How We Refute Claims: Automatic Fact-Checking through Flaw Identification and Explanation

Wei-Yu Kao, An-Zi Yen
1/27/2024
cs.CL

No official code found. Best implementation: NYCU-NLP-Lab/FlawCheck (2 stars, MIT, last commit 2024-06-20).

Abstract

Automated fact-checking is a crucial task in the governance of internet content. Although various studies utilize advanced models to tackle this issue, a significant gap persists in addressing complex real-world rumors and deceptive claims. To address this challenge, this paper explores the novel task of flaw-oriented fact-checking, including aspect generation and flaw identification. We also introduce RefuteClaim, a new framework designed specifically for this task. Given the absence of an existing dataset, we present FlawCheck, a dataset created by extracting and transforming insights from expert reviews into relevant aspects and identified flaws. The experimental results underscore the efficacy of RefuteClaim, particularly in classifying and elucidating false claims.

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Code Implementations(1)

NYCU-NLP-Lab/FlawCheckCommunity100%
MIT

Repository for the RefuteClaim paper and the FlawCheck dataset, focusing on the tasks of veracity classification and justification generation within the domain of automatic fact-checking.

20Shell, PythonJan 25, 20242 years agoMIT

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Cite this paper

@article{kao2024refute,
  title  = {How We Refute Claims: Automatic Fact-Checking through Flaw Identification and Explanation},
  author = {Wei-Yu Kao and An-Zi Yen},
  year   = {2024},
  eprint = {2401.15312},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.15312v1}
}

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