@inproceedings{trivedi-etal-2025-sarang,
    title = "Sarang at {F}in{C}ausal 2025: Contextual {QA} for Financial Causality Detection Combining Extractive and Generative Models",
    author = "Trivedi, Avinash  and
      Toshniwal, Gauri  and
      S, Sangeetha  and
      Balasundaram, S R.",
    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://preview.aclanthology.org/fix-sig-urls/2025.finnlp-1.25/",
    pages = "242--247",
    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{\%}."
}