@inproceedings{kabir-etal-2025-bidirectional,
title = "Bidirectional Reasoning Supervision for Multilingual Financial Decision Making",
author = "Kabir, Muhammad Rafsan and
Ahad, Jawad Ibn and
Krambroeckers, Robin and
Ahmed, Silvia and
Elahi, M M Lutfe and
Mohammed, Nabeel and
Rahman, Shafin",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.111/",
pages = "1576--1587",
ISBN = "979-8-89176-333-3",
abstract = "Large Language Models have achieved great success in tasks like sentiment analysis, machine translation, and question answering, yet their effectiveness in the multilingual financial domain remains less explored. This study explores the potential of generative LLMs for classifying financial sustainability in four diverse languages: English, Hindi, Bengali, and Telugu, representing low, medium, and high-resource language categories. We propose a novel fine-tuning approach that integrates both positive and negative rationales alongside classification labels. Unlike existing approaches, our method improves classification performance by incorporating structured bidirectional reasoning into financial decision-making. Extensive evaluations demonstrate that the proposed approach consistently outperforms prior methods across all four languages, establishing new benchmark results for multilingual financial NLP. Notably, it also enables smaller models to achieve competitive or even superior performance compared to significantly larger models fine-tuned with conventional methods, demonstrating its suitability for industry applications."
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%0 Conference Proceedings
%T Bidirectional Reasoning Supervision for Multilingual Financial Decision Making
%A Kabir, Muhammad Rafsan
%A Ahad, Jawad Ibn
%A Krambroeckers, Robin
%A Ahmed, Silvia
%A Elahi, M. M. Lutfe
%A Mohammed, Nabeel
%A Rahman, Shafin
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F kabir-etal-2025-bidirectional
%X Large Language Models have achieved great success in tasks like sentiment analysis, machine translation, and question answering, yet their effectiveness in the multilingual financial domain remains less explored. This study explores the potential of generative LLMs for classifying financial sustainability in four diverse languages: English, Hindi, Bengali, and Telugu, representing low, medium, and high-resource language categories. We propose a novel fine-tuning approach that integrates both positive and negative rationales alongside classification labels. Unlike existing approaches, our method improves classification performance by incorporating structured bidirectional reasoning into financial decision-making. Extensive evaluations demonstrate that the proposed approach consistently outperforms prior methods across all four languages, establishing new benchmark results for multilingual financial NLP. Notably, it also enables smaller models to achieve competitive or even superior performance compared to significantly larger models fine-tuned with conventional methods, demonstrating its suitability for industry applications.
%U https://aclanthology.org/2025.emnlp-industry.111/
%P 1576-1587
Markdown (Informal)
[Bidirectional Reasoning Supervision for Multilingual Financial Decision Making](https://aclanthology.org/2025.emnlp-industry.111/) (Kabir et al., EMNLP 2025)
ACL
- Muhammad Rafsan Kabir, Jawad Ibn Ahad, Robin Krambroeckers, Silvia Ahmed, M M Lutfe Elahi, Nabeel Mohammed, and Shafin Rahman. 2025. Bidirectional Reasoning Supervision for Multilingual Financial Decision Making. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1576–1587, Suzhou (China). Association for Computational Linguistics.