Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation
This addresses the problem of misinformation in financial reporting for domain experts and end-users, but it is incremental as it introduces a new dataset rather than a novel method.
The authors tackled the lack of quality datasets for financial fact-checking by proposing Fin-Fact, a benchmark dataset with multimodal content and professional annotations, which aims to combat misinformation and enhance transparency in finance.
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/.