@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Causal Connections: Leveraging Multilingual Fine-Tuning for Financial QA@FinCausal 2026
%A Gautam, Akash Kumar
%A Hamotskyi, Serhii
%A Hänig, Christian
%Y El-Haj, Mo
%Y Moreno Sandoval, Antonio
%Y Garcia-Serrano, Ana
%Y Chen, Chung-Chi
%Y Rayson, Paul
%Y Torterolo Orta, Yanco Amor
%Y Martinez, Paloma
%Y Porta, Jordi
%S The 7th Financial Narrative Processing Workshop
%D 2026
%8 May
%I European Language Resources Association (ELRA)
%C Palma de Mallorca, Spain
%F gautam-etal-2026-causal
%X 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.
%R 10.63317/4v9j8247boo3
%U https://aclanthology.org/2026.fnp-1.13/
%U https://doi.org/10.63317/4v9j8247boo3
%P 132-138
Markdown (Informal)
[Causal Connections: Leveraging Multilingual Fine-Tuning for Financial QA@FinCausal 2026](https://aclanthology.org/2026.fnp-1.13/) (Gautam et al., FNP 2026)
ACL