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

TL;DR

Fin-Fact is a new multimodal dataset with professional annotations designed to improve fact-checking and explanation generation in the financial domain, addressing the lack of quality datasets and combating misinformation.

Contribution

It introduces Fin-Fact, a comprehensive multimodal dataset with expert annotations for financial fact-checking and explanation generation, advancing research in this underexplored area.

Findings

Provides a benchmark for multimodal financial fact-checking

Includes professional annotations and justifications

Enhances transparency and trust in financial information

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/.