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Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation

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arxiv 2309.08793 v2 pith:YNHS6E66 submitted 2023-09-15 cs.AI cs.CEcs.LG

classification cs.AIcs.CEcs.LG
keywords fin-factdatasetdomainfact-checkingfinancialmultimodalbenchmarkcredibility
verification ladder T0 review T1 audit T2 compute T3 formal
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Fact-checking in financial domain is under explored, and there is a shortage of quality dataset in this domain. In this paper, we propose Fin-Fact, a benchmark dataset for multimodal fact-checking within the financial domain. Notably, it includes professional fact-checker annotations and justifications, providing expertise and credibility. With its multimodal nature encompassing both textual and visual content, Fin-Fact provides complementary information sources to enhance factuality analysis. Its primary objective is combating misinformation in finance, fostering transparency, and building trust in financial reporting and news dissemination. By offering insightful explanations, Fin-Fact empowers users, including domain experts and end-users, to understand the reasoning behind fact-checking decisions, validating claim credibility, and fostering trust in the fact-checking process. The Fin-Fact dataset, along with our experimental codes is available at https://github.com/IIT-DM/Fin-Fact/.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification

    cs.CL 2025-06 conditional novelty 7.0 of 10

    SciVer is the first benchmark for multimodal scientific claim verification over full paper context, and current foundation models score about 16 points below expert humans.

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