@inproceedings{chantangphol-etal-2025-finmind,
title = "{F}in{M}ind-{Y}-Me at the Regulations Challenge Task: Financial Mind Your Meaning based on {TH}a{LLE}",
author = "Chantangphol, Pantid and
Balee, Pornchanan and
Sucharitpongpan, Kantapong and
Saetia, Chanatip and
Chalothorn, Tawunrat",
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-1.41/",
pages = "349--362",
abstract = "This paper presents our submission to the COLING 2025 regulation challenge, focusing on nine tasks in the regulatory and financial domains. The challenge aims to advance large language models beyond general-purpose capabilities, adapting them for regulatory and financial tasks using a unified framework of task-specific prompts and input templates. We propose a sequential fine-tuning approach that integrates reasoning-based training, tailored system prompts, and Chain-of-Thought (CoT) inference to optimize task-specific performance. This method improves accuracy and reliability across diverse tasks. Notably, CoT inference demonstrates exceptional effectiveness in handling complex scenarios and tasks requiring specific answer patterns, such as named entity recognition and financial calculations. Our model achieved an overall score of 54.801{\%}, ranking 1st among all teams and becoming the top performer in the challenge. These results highlight the effectiveness of sequential fine-tuning, advanced reasoning techniques, and fine-tuned prompts in improving performance and scalability for complex regulatory and financial applications."
}
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%0 Conference Proceedings
%T FinMind-Y-Me at the Regulations Challenge Task: Financial Mind Your Meaning based on THaLLE
%A Chantangphol, Pantid
%A Balee, Pornchanan
%A Sucharitpongpan, Kantapong
%A Saetia, Chanatip
%A Chalothorn, Tawunrat
%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 chantangphol-etal-2025-finmind
%X This paper presents our submission to the COLING 2025 regulation challenge, focusing on nine tasks in the regulatory and financial domains. The challenge aims to advance large language models beyond general-purpose capabilities, adapting them for regulatory and financial tasks using a unified framework of task-specific prompts and input templates. We propose a sequential fine-tuning approach that integrates reasoning-based training, tailored system prompts, and Chain-of-Thought (CoT) inference to optimize task-specific performance. This method improves accuracy and reliability across diverse tasks. Notably, CoT inference demonstrates exceptional effectiveness in handling complex scenarios and tasks requiring specific answer patterns, such as named entity recognition and financial calculations. Our model achieved an overall score of 54.801%, ranking 1st among all teams and becoming the top performer in the challenge. These results highlight the effectiveness of sequential fine-tuning, advanced reasoning techniques, and fine-tuned prompts in improving performance and scalability for complex regulatory and financial applications.
%U https://aclanthology.org/2025.finnlp-1.41/
%P 349-362
Markdown (Informal)
[FinMind-Y-Me at the Regulations Challenge Task: Financial Mind Your Meaning based on THaLLE](https://aclanthology.org/2025.finnlp-1.41/) (Chantangphol et al., FinNLP 2025)
ACL
- Pantid Chantangphol, Pornchanan Balee, Kantapong Sucharitpongpan, Chanatip Saetia, and Tawunrat Chalothorn. 2025. FinMind-Y-Me at the Regulations Challenge Task: Financial Mind Your Meaning based on THaLLE. 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 349–362, Abu Dhabi, UAE. Association for Computational Linguistics.