arXiv:2602.10384v4 Announce Type: replace 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 Scribe Finance, the first multimodal benchmark for evaluating French financial document understanding. The dataset contains 1,204 expert-validated questions spanning text extraction, tabl
The proliferation of vision-language models necessitates robust evaluation benchmarks in specialized, non-English domains, particularly given the critical nature of financial document understanding.
Evaluating multimodal AI models in non-English financial contexts is crucial for mitigating real-world errors and enabling wider, more reliable AI adoption in highly regulated sectors globally.
The introduction of Scribe Finance provides a specific benchmark for French financial document understanding, highlighting the need for localized and specialized AI development beyond English-centric models.
- · AI developers specializing in non-English NLP
- · Financial institutions seeking localized AI solutions
- · European AI research institutions
- · Multilingual AI platforms
- · AI models without multilingual or domain-specific training
- · Companies relying solely on English-centric AI for global operations
Improved performance and reliability of AI in specific financial document analysis for non-English languages.
Increased demand for specialized, culturally aware AI solutions tailored to diverse regulatory and linguistic environments.
Potential for new regulatory frameworks around AI accountability and performance in critical, distinct national and linguistic contexts.
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Read at arXiv cs.CL