因果联系：利用多语言微调进行金融问答@FinCausal 2026
Causal Connections: Leveraging Multilingual Fine-Tuning for Financial QA@FinCausal 2026
AI总结：
本文通过对比编码器、编码器-解码器和仅解码器模型，发现监督微调效果最佳，GPT-4.1 Mini在英西双语微调后取得英语最高分和西班牙语第三名。
摘要：
本文描述了HSA_CORAL团队在FinCausal 2026共享任务中的提交，该任务旨在通过英语和西班牙语的抽取式问答从金融叙述中提取因果关系。我们比较了三个模型家族：(i) 仅编码器的多语言BERT词元标注，(ii) 编码器-解码器的多语言BART生成，以及(iii) 仅解码器的LLM（Llama 3.1和GPT变体），使用提示优化、少样本演示和监督微调。在各种设置下，提示和少样本示例取得了有竞争力的性能，而监督微调带来了最大的提升。我们最好的系统，在英西组合训练数据上微调的GPT-4.1 Mini，在共享任务的LLM作为评判指标下，英语子任务获得并列最高分（4.8140），西班牙语排名第三（4.7753）。总体而言，结果凸显了任务特定适应和多语言微调在金融因果问答跨语言迁移中的价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
