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            <title>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)</title>
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    <abstract>Financial misinformation poses a significant threat to investment decisions and market stability. Recently, the application of Large Language Models (LLMs) for detecting financial misinformation has gained considerable attention within the natural language processing (NLP) community. The Financial Misinformation Detection (FMD) challenge @ Coling 2025 serves as a valuable platform for collaboration and innovation. This paper presents our solution to FMD challenge. Our approach involves using search engines to retrieve the summarized high-quality information as supporting evidence and designing a financial domain-specific chain-of-thought to enhance the reasoning capabilities of LLMs. We evaluated our method on both commercial closed-source LLMs (GPT-family) and open-source models (Llama-3.1-8B and QWen). The experimental results domonstrate that the proposed method improves veracity prediction performance. However, the quality of the generated explanations remains relatively poor. In the paper, we present the experimental findings and provides an in depth analysis of these results.</abstract>
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%0 Conference Proceedings
%T Capybara at the Financial Misinformation Detection Challenge Task: Chain-of-Thought Enhanced Financial Misinformation Detection
%A Cao, Yupeng
%A Li, Haohang
%A Yu, Yangyang
%A Javaji, Shashidhar Reddy
%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 cao-etal-2025-capybara
%X Financial misinformation poses a significant threat to investment decisions and market stability. Recently, the application of Large Language Models (LLMs) for detecting financial misinformation has gained considerable attention within the natural language processing (NLP) community. The Financial Misinformation Detection (FMD) challenge @ Coling 2025 serves as a valuable platform for collaboration and innovation. This paper presents our solution to FMD challenge. Our approach involves using search engines to retrieve the summarized high-quality information as supporting evidence and designing a financial domain-specific chain-of-thought to enhance the reasoning capabilities of LLMs. We evaluated our method on both commercial closed-source LLMs (GPT-family) and open-source models (Llama-3.1-8B and QWen). The experimental results domonstrate that the proposed method improves veracity prediction performance. However, the quality of the generated explanations remains relatively poor. In the paper, we present the experimental findings and provides an in depth analysis of these results.
%U https://aclanthology.org/2025.finnlp-1.38/
%P 321-325

Markdown (Informal)

[Capybara at the Financial Misinformation Detection Challenge Task: Chain-of-Thought Enhanced Financial Misinformation Detection](https://aclanthology.org/2025.finnlp-1.38/) (Cao et al., FinNLP 2025)

• Capybara at the Financial Misinformation Detection Challenge Task: Chain-of-Thought Enhanced Financial Misinformation Detection (Cao et al., FinNLP 2025)

ACL

• Yupeng Cao, Haohang Li, Yangyang Yu, and Shashidhar Reddy Javaji. 2025. Capybara at the Financial Misinformation Detection Challenge Task: Chain-of-Thought Enhanced Financial Misinformation Detection. 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 321–325, Abu Dhabi, UAE. Association for Computational Linguistics.