Causal Connections: Leveraging Multilingual Fine-Tuning for Financial QA@FinCausal 2026

Team HSA_CORAL submitted to the FinCausal 2026 shared task on extracting cause-effect relations from financial narratives via extractive question answering in English and Spanish. Their best system, GPT-4.1 Mini fine-tuned on combined English and Spanish training data, achieved a tied highest score on the English subtask and ranked third on Spanish. The results highlight the value of task-specific adaptation and multilingual fine-tuning for cross-lingual transfer in financial causality QA.

arXiv:2606.27446v1 Announce Type: new 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.