@inproceedings{singh-etal-2025-ask,
title = "Ask Asper at the Financial Misinformation Detection Challenge Task: Enhancing Financial Decision-Making: A Dual Approach Using Explainable {LLM}s for Misinformation Detection",
author = "Singh, Sonal and
Mehta, Rahul and
Gupta, Yadunath and
Chowdhury, Soudip Roy",
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-1.32/",
pages = "283--287",
abstract = "The integrity of the market and investor con- fidence are seriously threatened by the prolif- eration of financial misinformation via digital media. Existing approaches such as fact check, lineage detection and others have demonstrated significant progress in detecting financial mis- information. In this paper, we present a novel two-stage framework leveraging large language models (LLMs) to identify and explain finan- cial misinformation. The framework first em- ploys a GPT-4 model fine-tuned on financial datasets to classify claims as {\textquotedblleft}True,{\textquotedblright} {\textquotedblleft}False,{\textquotedblright} or {\textquotedblleft}Not Enough Information{\textquotedblright} by analyzing rel- evant financial context. To enhance classifi- cation reliability, a second LLM serves as a verification layer, examining and refining the initial model`s predictions. This dual-model approach ensures greater accuracy in misinfor- mation detection through cross-validation. Beyond classification, our methodology empha- sizes generating clear, concise, and actionable explanations that enable users to understand the reasoning behind each determination. By com- bining robust misinformation detection with interpretability, our paradigm advances AI sys- tem transparency and accountability, providing valuable support to investors, regulators, and financial stakeholders in mitigating misinfor- mation risks."
}
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<abstract>The integrity of the market and investor con- fidence are seriously threatened by the prolif- eration of financial misinformation via digital media. Existing approaches such as fact check, lineage detection and others have demonstrated significant progress in detecting financial mis- information. In this paper, we present a novel two-stage framework leveraging large language models (LLMs) to identify and explain finan- cial misinformation. The framework first em- ploys a GPT-4 model fine-tuned on financial datasets to classify claims as “True,” “False,” or “Not Enough Information” by analyzing rel- evant financial context. To enhance classifi- cation reliability, a second LLM serves as a verification layer, examining and refining the initial model‘s predictions. This dual-model approach ensures greater accuracy in misinfor- mation detection through cross-validation. Beyond classification, our methodology empha- sizes generating clear, concise, and actionable explanations that enable users to understand the reasoning behind each determination. By com- bining robust misinformation detection with interpretability, our paradigm advances AI sys- tem transparency and accountability, providing valuable support to investors, regulators, and financial stakeholders in mitigating misinfor- mation risks.</abstract>
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%0 Conference Proceedings
%T Ask Asper at the Financial Misinformation Detection Challenge Task: Enhancing Financial Decision-Making: A Dual Approach Using Explainable LLMs for Misinformation Detection
%A Singh, Sonal
%A Mehta, Rahul
%A Gupta, Yadunath
%A Chowdhury, Soudip Roy
%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 singh-etal-2025-ask
%X The integrity of the market and investor con- fidence are seriously threatened by the prolif- eration of financial misinformation via digital media. Existing approaches such as fact check, lineage detection and others have demonstrated significant progress in detecting financial mis- information. In this paper, we present a novel two-stage framework leveraging large language models (LLMs) to identify and explain finan- cial misinformation. The framework first em- ploys a GPT-4 model fine-tuned on financial datasets to classify claims as “True,” “False,” or “Not Enough Information” by analyzing rel- evant financial context. To enhance classifi- cation reliability, a second LLM serves as a verification layer, examining and refining the initial model‘s predictions. This dual-model approach ensures greater accuracy in misinfor- mation detection through cross-validation. Beyond classification, our methodology empha- sizes generating clear, concise, and actionable explanations that enable users to understand the reasoning behind each determination. By com- bining robust misinformation detection with interpretability, our paradigm advances AI sys- tem transparency and accountability, providing valuable support to investors, regulators, and financial stakeholders in mitigating misinfor- mation risks.
%U https://aclanthology.org/2025.finnlp-1.32/
%P 283-287
Markdown (Informal)
[Ask Asper at the Financial Misinformation Detection Challenge Task: Enhancing Financial Decision-Making: A Dual Approach Using Explainable LLMs for Misinformation Detection](https://aclanthology.org/2025.finnlp-1.32/) (Singh et al., FinNLP 2025)
ACL