Abstract:Financial decision-making in multilingual settings demands accurate numerical reasoning grounded in diverse modalities, yet existing benchmarks largely overlook this high-stakes, real-world challenge, especially for Indic languages. We introduce FinVQA, a benchmark for evaluating financial numerical and multimodal reasoning in multilingual Indic contexts. FinVQA spans English, Hindi, Bengali, Marathi, Gujarati, and Tamil, and comprises 18,900 samples across 14 financial domains. The dataset captures diverse reasoning paradigms under realistic constraints, and is structured across three difficulty levels (easy, moderate, hard) and four question formats: multiple choice, fill-in-the-blank, table matching, and true/false. To address these challenges, we propose FIND, a framework that combines supervised fine-tuning with constraint-aware decoding to promote faithful numerical reasoning, robust multimodal grounding, and structured decision-making. Together, FinVQA and FIND establish a rigorous evaluation and modeling paradigm for high-stakes multilingual multimodal financial reasoning.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.13330 [cs.CL] |
| (or arXiv:2605.13330v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.13330 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Vaibhav Vishal [view email]
[v1]
Wed, 13 May 2026 10:45:42 UTC (9,435 KB)