@inproceedings{gautam-etal-2026-causal,
    title = "Causal Connections: Leveraging Multilingual Fine-Tuning for Financial {QA}@{F}in{C}ausal 2026",
    author = {Gautam, Akash Kumar  and
      Hamotskyi, Serhii  and
      H{\"a}nig, Christian},
    editor = "El-Haj, Mo  and
      Moreno Sandoval, Antonio  and
      Garcia-Serrano, Ana  and
      Chen, Chung-Chi  and
      Rayson, Paul  and
      Torterolo Orta, Yanco Amor  and
      Martinez, Paloma  and
      Porta, Jordi",
    booktitle = "The 7th Financial Narrative Processing Workshop",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/revision-workflow/2026.fnp-1.13/",
    doi = "10.63317/4v9j8247boo3",
    pages = "132--138",
    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, but supervised fine-tuning is the main driver of improvement. Our best system, GPT-4.1 Mini fine-tuned on combined English and Spanish training data, achieves the highest (tied) score on English (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."
}