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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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            <namePart type="given">Chung-Chi</namePart>
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    <abstract>This paper describes our approach for the FinCausal 2025 English Shared Task, aimed at detecting and extracting causal relationships from the financial text. The task involved answering context-driven questions to identify causes or effects within specified text segments. Our method utilized a consciousAI RoBERTa-base encoder model, fine-tuned on the SQuADx dataset. We further fine-tuned it using the FinCausal 2025 development set. To enhance the quality and contextual relevance of the answers, we passed outputs from the extractive model through Gemma2-9B, a generative large language model, for answer refinement. This hybrid approach effectively addressed the task‘s requirements, showcasing the strength of combining extractive and generative models. We (Team name: Sarang) achieved outstanding results, securing 3rd rank with a Semantic Answer Similarity (SAS) score of 96.74% and an Exact Match (EM) score of 70.14%.</abstract>
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%0 Conference Proceedings
%T Sarang at FinCausal 2025: Contextual QA for Financial Causality Detection Combining Extractive and Generative Models
%A Trivedi, Avinash
%A Toshniwal, Gauri
%A S, Sangeetha
%A Balasundaram, S. R.
%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 trivedi-etal-2025-sarang
%X This paper describes our approach for the FinCausal 2025 English Shared Task, aimed at detecting and extracting causal relationships from the financial text. The task involved answering context-driven questions to identify causes or effects within specified text segments. Our method utilized a consciousAI RoBERTa-base encoder model, fine-tuned on the SQuADx dataset. We further fine-tuned it using the FinCausal 2025 development set. To enhance the quality and contextual relevance of the answers, we passed outputs from the extractive model through Gemma2-9B, a generative large language model, for answer refinement. This hybrid approach effectively addressed the task‘s requirements, showcasing the strength of combining extractive and generative models. We (Team name: Sarang) achieved outstanding results, securing 3rd rank with a Semantic Answer Similarity (SAS) score of 96.74% and an Exact Match (EM) score of 70.14%.
%U https://aclanthology.org/2025.finnlp-1.25/
%P 242-247

Markdown (Informal)

[Sarang at FinCausal 2025: Contextual QA for Financial Causality Detection Combining Extractive and Generative Models](https://aclanthology.org/2025.finnlp-1.25/) (Trivedi et al., FinNLP 2025)

• Sarang at FinCausal 2025: Contextual QA for Financial Causality Detection Combining Extractive and Generative Models (Trivedi et al., FinNLP 2025)

ACL

• Avinash Trivedi, Gauri Toshniwal, Sangeetha S, and S R. Balasundaram. 2025. Sarang at FinCausal 2025: Contextual QA for Financial Causality Detection Combining Extractive and Generative 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 242–247, Abu Dhabi, UAE. Association for Computational Linguistics.