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

Aman Rangapur, Haoran Wang, Ling Jian, Kai Shu
9/15/2023
cs.AIcs.CEcs.LG
benchmark-dataset

Official code: IIT-DM/Fin-Fact (25 stars, MIT, last commit 2024-09-17).

Abstract

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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Code Implementations(1)

IIT-DM/Fin-FactOfficial100%
MIT

A Benchmark Dataset for Multimodal Scientific Fact Checking

2511PythonApr 17, 20232 years agoMIT
datasetdatasetsexplaination-generationfact-checkfact-checking+7 more

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Cite this paper

@article{rangapur2023finfact,
  title  = {Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation},
  author = {Aman Rangapur and Haoran Wang and Ling Jian and Kai Shu},
  year   = {2023},
  eprint = {2309.08793},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.08793v2}
}

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