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

• URL: http://arxiv.org/abs/2606.27446v1
• Date: Thu, 25 Jun 2026 18:17:04 GMT
• Title: Causal Connections: Leveraging Multilingual Fine-Tuning for Financial QA@FinCausal 2026
• Abstract summary: 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.<n>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.<n>Across settings, prompting and few-shot examples yield competitive performance, while supervised fine-tuning provides the largest gains
• Score: 1.8723525304649937
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• IGT @ FinMMEval 2026 Task 2: Question-Type Prompting with Targeted Extraction for Multilingual Financial QA [0.0]
  PolyFiQA Task 2 was a multilingual financial question answering task over English SEC filings and multilingual news articles.<n>Our central observation is that the 344 development questions divide into two families requiring fundamentally different approaches.<n>A dataset analysis reveals that 17-18 of 19 ground-truth reference answers per synthesis type share an exact evidence label prefix.<n>The final system achieves development ROUGE-1 approximately 0.395, a 60% relative improvement over a generic RAG baseline.
  arXiv  Detail & Related papers  (2026-09-08T02:23:31Z)
• MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application [118.63802040274999]
  MultiFinBen is the first expert-annotated multilingual (five languages) and multimodal benchmark for evaluating LLMs in realistic financial contexts.<n>Financial reasoning tests cross-lingual evidence integration from filings and news, and financial OCR, which extracts structured text from scanned documents.<n> evaluating 21 leading LLMs shows that even frontier multimodal models like GPT-4o achieve only 46.01% overall, stronger on vision and audio but dropping sharply in multilingual settings.
  arXiv  Detail & Related papers  (2025-06-16T22:01:49Z)
• M$^3$FinMeeting: A Multilingual, Multi-Sector, and Multi-Task Financial Meeting Understanding Evaluation Dataset [18.752133381125564]
  $texttM$3$FinMeeting$ is a multilingual, multi-sector, and multi-task dataset designed for financial meeting understanding.<n>First, it supports English, Chinese, and Japanese, enhancing comprehension of financial discussions in diverse linguistic contexts.<n>Second, it encompasses various industry sectors defined by the Global Industry Classification Standard (GICS)<n>Third, it includes three tasks: summarization, question-answer (QA) pair extraction, and question answering, facilitating a more realistic and comprehensive evaluation of understanding.
  arXiv  Detail & Related papers  (2025-06-03T06:41:09Z)
• MSA at SemEval-2025 Task 3: High Quality Weak Labeling and LLM Ensemble Verification for Multilingual Hallucination Detection [0.0]
  This paper describes our submission for SemEval-2025 Task 3: Mu-SHROOM, the Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes.<n>The task involves detecting hallucinated spans in text generated by instruction-tuned Large Language Models (LLMs) across multiple languages.<n>Our system ranked 1st in Arabic and Basque, 2nd in German, Swedish, and Finnish, and 3rd in Czech, Farsi, and French.
  arXiv  Detail & Related papers  (2025-05-27T08:26:17Z)
• COMI-LINGUA: Expert Annotated Large-Scale Dataset for Multitask NLP in Hindi-English Code-Mixing [1.2242530642524063]
  COMI-LINGUA is the largest manually annotated Hindi-English code-mixed dataset.<n>It comprises 125K+ high-quality instances across five core NLP tasks.<n>Each instance is annotated by three bilingual annotators, yielding over 376K expert annotations.
  arXiv  Detail & Related papers  (2025-03-27T16:36:39Z)
• D\'olares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English [67.48541936784501]
  Tois'on de Oro is the first framework that establishes instruction datasets, finetuned LLMs, and evaluation benchmark for financial LLMs in Spanish joint with English. We construct a rigorously curated bilingual instruction dataset including over 144K Spanish and English samples from 15 datasets covering 7 tasks. We evaluate our model and existing LLMs using FLARE-ES, the first comprehensive bilingual evaluation benchmark with 21 datasets covering 9 tasks.
  arXiv  Detail & Related papers  (2024-02-12T04:50:31Z)
• SCALE: Synergized Collaboration of Asymmetric Language Translation Engines [105.8983433641208]
  We introduce a collaborative framework that connects compact Specialized Translation Models (STMs) and general-purpose Large Language Models (LLMs) as one unified translation engine. By introducing translation from STM into the triplet in-context demonstrations, SCALE unlocks refinement and pivoting ability of LLM. Our experiments show that SCALE significantly outperforms both few-shot LLMs (GPT-4) and specialized models (NLLB) in challenging low-resource settings.
  arXiv  Detail & Related papers  (2023-09-29T08:46:38Z)
• Extrapolating Large Language Models to Non-English by Aligning Languages [109.09051737966178]
  Existing large language models show disparate capability across different languages. In this paper, we empower pre-trained LLMs on non-English languages by building semantic alignment across languages.
  arXiv  Detail & Related papers  (2023-08-09T13:32:06Z)
• Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning [98.60739735409243]
  Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks. We introduce XSGD for cross-lingual alignment pretraining, a parallel and large-scale multilingual conversation dataset. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts.
  arXiv  Detail & Related papers  (2023-04-03T18:46:01Z)
• Multilingual Machine Translation Systems from Microsoft for WMT21 Shared Task [95.06453182273027]
  This report describes Microsoft's machine translation systems for the WMT21 shared task on large-scale multilingual machine translation. Our model submissions to the shared task were with DeltaLMnotefooturlhttps://aka.ms/deltalm, a generic pre-trained multilingual-decoder model. Our final submissions ranked first on three tracks in terms of the automatic evaluation metric.
  arXiv  Detail & Related papers  (2021-11-03T09:16:17Z)
• Ensemble Fine-tuned mBERT for Translation Quality Estimation [0.0]
  In this paper, we discuss our submission to the WMT 2021 QE Shared Task. Our proposed system is an ensemble of multilingual BERT (mBERT)-based regression models. It demonstrates comparable performance with respect to the Pearson's correlation and beats the baseline system in MAE/ RMSE for several language pairs.
  arXiv  Detail & Related papers  (2021-09-08T20:13:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.