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HSA_CORAL's GPT-4.1 Mini leads FinCausal 2026 financial causality task

By PulseAugur Editorial · [2 sources] ·

A research paper details HSA_CORAL's approach to the FinCausal 2026 shared task, focusing on extracting cause-effect relationships from financial texts. The team explored three model families: multilingual BERT for token tagging, multilingual BART for generation, and decoder-only LLMs like Llama 3.1 and GPT variants. Their best-performing system, GPT-4.1 Mini, achieved top scores in English and Spanish by leveraging supervised fine-tuning on combined multilingual data. AI

IMPACT Demonstrates the effectiveness of multilingual fine-tuning and task-specific adaptation for cross-lingual financial causality extraction.

RANK_REASON The cluster describes a research paper detailing a submission to a specific academic task, including model comparisons and performance metrics.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

HSA_CORAL's GPT-4.1 Mini leads FinCausal 2026 financial causality task

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Akash Kumar Gautam, Serhii Hamotskyi, Christian H\"anig ·

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

    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 fa…

  2. arXiv cs.CL TIER_1 English(EN) · Christian Hänig ·

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

    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 mult…

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