Causal Connections: Leveraging Multilingual Fine-Tuning for Financial QA@FinCausal 2026
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Computer Science > Computation and Language
Title:Causal Connections: Leveraging Multilingual Fine-Tuning for Financial QA@FinCausal 2026
Abstract:This paper describes team HSA_CORAL's submission to the FinCausal 2026 shared task on extracting cause-effect relations from financial narratives via extractive question answering in English and Spanish. We compare three modeling families: (i) encoder-only token tagging with multilingual BERT, (ii) encoder-decoder generation with multilingual BART, and (iii) decoder-only LLMs (Llama 3.1 and GPT variants) using prompt refinement, few-shot demonstrations, and supervised fine-tuning. Across settings, prompting and few-shot examples yield competitive performance, while supervised fine-tuning provides the largest gains. Our best system, GPT-4.1 Mini fine-tuned on combined English and Spanish training data, achieves a tied highest score on the English subtask (score 4.8140) and ranks third on Spanish (score 4.7753) under the shared task's LLM-as-a-judge metric. Overall, the results highlight the value of task-specific adaptation and multilingual fine-tuning for cross-lingual transfer in financial causality QA.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.27446 [cs.CL] |
| (or arXiv:2606.27446v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.27446
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Proceedings of the 7th Financial Narrative Processing Workshop (FNP 2026) at LREC 2026, pp. 132-138, 2026 |
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