@inproceedings{liu-etal-2025-finnlp,
title = "{F}in{NLP}-{FNP}-{LLMF}in{L}egal-2025 Shared Task: Financial Misinformation Detection Challenge Task",
author = "Liu, Zhiwei and
Wang, Keyi and
Bao, Zhuo and
Zhang, Xin and
Dong, Jiping and
Yang, Kailai and
Kabir, Mohsinul and
Giannouris, Polydoros and
Xing, Rui and
Seongchan, Park and
Kim, Jaehong and
Li, Dong and
Xie, Qianqian and
Ananiadou, Sophia",
editor = "Chen, Chung-Chi and
Moreno-Sandoval, Antonio and
Huang, Jimin and
Xie, Qianqian and
Ananiadou, Sophia and
Chen, Hsin-Hsi",
booktitle = "Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.finnlp-1.30/",
pages = "271--276",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T FinNLP-FNP-LLMFinLegal-2025 Shared Task: Financial Misinformation Detection Challenge Task
%A Liu, Zhiwei
%A Wang, Keyi
%A Bao, Zhuo
%A Zhang, Xin
%A Dong, Jiping
%A Yang, Kailai
%A Kabir, Mohsinul
%A Giannouris, Polydoros
%A Xing, Rui
%A Seongchan, Park
%A Kim, Jaehong
%A Li, Dong
%A Xie, Qianqian
%A Ananiadou, Sophia
%Y Chen, Chung-Chi
%Y Moreno-Sandoval, Antonio
%Y Huang, Jimin
%Y Xie, Qianqian
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%S Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F liu-etal-2025-finnlp
%X 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.
%U https://aclanthology.org/2025.finnlp-1.30/
%P 271-276
Markdown (Informal)
[FinNLP-FNP-LLMFinLegal-2025 Shared Task: Financial Misinformation Detection Challenge Task](https://aclanthology.org/2025.finnlp-1.30/) (Liu et al., FinNLP 2025)
ACL
- Zhiwei Liu, Keyi Wang, Zhuo Bao, Xin Zhang, Jiping Dong, Kailai Yang, Mohsinul Kabir, Polydoros Giannouris, Rui Xing, Park Seongchan, Jaehong Kim, Dong Li, Qianqian Xie, and Sophia Ananiadou. 2025. FinNLP-FNP-LLMFinLegal-2025 Shared Task: Financial Misinformation Detection Challenge Task. In Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal), pages 271–276, Abu Dhabi, UAE. Association for Computational Linguistics.