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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>Large Language Models (LLMs) are capable of producing highly fluent and convincing text; however, they can sometimes include factual errors and misleading information. Consequently, LLMs have emerged as tools for the rapid and cost-effective generation of financial misinformation, enabling bad actors to harm individual investors and attempt to manipulate markets. In this study, we instruction-tune Generative Pre-trained Transformers (GPT-4o-mini) to detect financial misinformation and produce concise explanations for why a given claim or statement is classified as misinformation, leveraging the contextual information provided. Our model achieved fourth place in Financial Misinformation Detection (FMD) shared task with a micro F1 score of 0.788 and a ROUGE-1 score of 0.743 on the private test set of FACT-checking within the FINancial domain (FIN-FACT) dataset provided by the shared task organizers.</abstract>
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%0 Conference Proceedings
%T Deloitte (Drocks) at the Financial Misinformation Detection Challenge Task: Enhancing Misinformation Detection through Instruction-Tuned Models
%A Abburi, Harika
%A Chandler, Alex
%A Bowen, Edward
%A Bhattacharya, Sanmitra
%A Pudota, Nirmala
%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 abburi-etal-2025-deloitte
%X Large Language Models (LLMs) are capable of producing highly fluent and convincing text; however, they can sometimes include factual errors and misleading information. Consequently, LLMs have emerged as tools for the rapid and cost-effective generation of financial misinformation, enabling bad actors to harm individual investors and attempt to manipulate markets. In this study, we instruction-tune Generative Pre-trained Transformers (GPT-4o-mini) to detect financial misinformation and produce concise explanations for why a given claim or statement is classified as misinformation, leveraging the contextual information provided. Our model achieved fourth place in Financial Misinformation Detection (FMD) shared task with a micro F1 score of 0.788 and a ROUGE-1 score of 0.743 on the private test set of FACT-checking within the FINancial domain (FIN-FACT) dataset provided by the shared task organizers.
%U https://aclanthology.org/2025.finnlp-1.37/
%P 313-320

Markdown (Informal)

[Deloitte (Drocks) at the Financial Misinformation Detection Challenge Task: Enhancing Misinformation Detection through Instruction-Tuned Models](https://aclanthology.org/2025.finnlp-1.37/) (Abburi et al., FinNLP 2025)

• Deloitte (Drocks) at the Financial Misinformation Detection Challenge Task: Enhancing Misinformation Detection through Instruction-Tuned Models (Abburi et al., FinNLP 2025)

ACL

• Harika Abburi, Alex Chandler, Edward Bowen, Sanmitra Bhattacharya, and Nirmala Pudota. 2025. Deloitte (Drocks) at the Financial Misinformation Detection Challenge Task: Enhancing Misinformation Detection through Instruction-Tuned Models. 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 313–320, Abu Dhabi, UAE. Association for Computational Linguistics.