FinNLP-FNP-LLMFinLegal-2025 Shared Task: Financial Misinformation Detection Challenge Task

Zhiwei Liu, Keyi Wang, Zhuo Bao, Xin Zhang, Jiping Dong, Kailai Yang, Mohsinul Kabir, Polydoros Giannouris, Rui Xing, Seongchan Park, Jaehong Kim, Dong Li, Qianqian Xie, Sophia Ananiadou

Abstract: Despite the promise of large language models (LLMs) in finance, their capabilities for financial misinformation detection (FMD) remain largely unexplored. To evaluate the capabilities of LLMs in FMD task, we introduce the financial misinformation detection shared task featured at COLING FinNLP-FNP-LLMFinLegal-2024, FMD Challenge. This challenge aims to evaluate the ability of LLMs to verify financial misinformation while generating plausible explanations. In this paper, we provide an overview of this task and dataset, summarize participants’ methods, and present their experimental evaluations, highlighting the effectiveness of LLMs in addressing the FMD task. To the best of our knowledge, the FMD Challenge is one of the first challenges for assessing LLMs in the field of FMD. Therefore, we provide detailed observations and draw conclusions for the future development of this field.

External IDs:dblp:conf/coling/0003WBZDYKGXPKL25