When Tables Go Crazy: Evaluating Multimodal Models on French Financial Documents

• URL: http://arxiv.org/abs/2602.10384v2
• Date: Thu, 12 Feb 2026 20:41:46 GMT
• Title: When Tables Go Crazy: Evaluating Multimodal Models on French Financial Documents
• Authors: Virginie Mouilleron, Théo Lasnier, Djamé Seddah,
• Abstract summary: Vision-language models (VLMs) perform well on many document understanding tasks, yet their reliability in specialized, non-English domains remains underexplored.<n>We introduce Multimodal Finance Eval, the first multimodal benchmark for evaluating French financial document understanding.<n>The dataset contains 1,204 expert-validated questions spanning text extraction, table comprehension, chart interpretation, and multi-turn conversational reasoning.
• Score: 3.4992819560032267
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-language models (VLMs) perform well on many document understanding tasks, yet their reliability in specialized, non-English domains remains underexplored. This gap is especially critical in finance, where documents mix dense regulatory text, numerical tables, and visual charts, and where extraction errors can have real-world consequences. We introduce Multimodal Finance Eval, the first multimodal benchmark for evaluating French financial document understanding. The dataset contains 1,204 expert-validated questions spanning text extraction, table comprehension, chart interpretation, and multi-turn conversational reasoning, drawn from real investment prospectuses, KIDs, and PRIIPs. We evaluate six open-weight VLMs (8B-124B parameters) using an LLM-as-judge protocol. While models achieve strong performance on text and table tasks (85-90% accuracy), they struggle with chart interpretation (34-62%). Most notably, multi-turn dialogue reveals a sharp failure mode: early mistakes propagate across turns, driving accuracy down to roughly 50% regardless of model size. These results show that current VLMs are effective for well-defined extraction tasks but remain brittle in interactive, multi-step financial analysis. Multimodal Finance Eval offers a challenging benchmark to measure and drive progress in this high-stakes setting.

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