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Sheffield NLP at FinCausal 2026: A Comparative Study of RAG Approaches and Fine-Tuning for Causal Q&A in Financial Texts
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Sheffield NLP at FinCausal 2026: A Comparative Study of RAG Approaches and Fine-Tuning for Causal Q&A in Financial Texts
This paper describes our approach to the FinCausal 2026 shared task, which addresses causal question answering from financial documents in English and Spanish. We investigated the effectiveness of fine-tuned generative models combined with Retrieval-Augmented Generation (RAG). Our approach compares five retrieval strategies across base and fine-tuned GPT-models (GPT-4.1-mini). RAG-based few-shot selection showed better performance than random sampling, particularly for the base model. In the FinCausal 2026 official run, this approach was ranked first in both the English and Spanish subtasks, obtaining LLM scores of 4.8140 and 4.8131 out of 5, respectively.
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