MultiFinBen: A Multilingual, Multimodal, and Difficulty-Aware Benchmark
  for Financial LLM Evaluation
Recent advances in large language models (LLMs) have accelerated progress in
financial NLP and applications, yet existing benchmarks remain limited to
monolingual and unimodal settings, often over-relying on simple tasks and
failing to reflect the complexity of real-world financial communication. We
introduce MultiFinBen, the first multilingual and multimodal benchmark tailored
to the global financial domain, evaluating LLMs across modalities (text,
vision, audio) and linguistic settings (monolingual, bilingual, multilingual)
on domain-specific tasks. We introduce two novel tasks, including PolyFiQA-Easy
and PolyFiQA-Expert, the first multilingual financial benchmarks requiring
models to perform complex reasoning over mixed-language inputs; and EnglishOCR
and SpanishOCR, the first OCR-embedded financial QA tasks challenging models to
extract and reason over information from visual-text financial documents.
Moreover, we propose a dynamic, difficulty-aware selection mechanism and curate
a compact, balanced benchmark rather than simple aggregation existing datasets.
Extensive evaluation of 22 state-of-the-art models reveals that even the
strongest models, despite their general multimodal and multilingual
capabilities, struggle dramatically when faced with complex cross-lingual and
multimodal tasks in financial domain. MultiFinBen is publicly released to
foster transparent, reproducible, and inclusive progress in financial studies
and applications.
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Xueqing Peng
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2025-06-18T16:15:27.517Z
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Xueqing Peng
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Xueqing
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Lingfei Qian
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Yan Wang
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2025-06-18T16:15:31.114Z
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Yan Wang
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YanAdjeNole
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Ruoyu Xiang
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Yueru He
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2025-06-18T16:15:33.127Z
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Yueru He
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Yueru1
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Yang Ren
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Mingyang Jiang
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2025-06-18T16:15:29.378Z
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Mingyang Jiang
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Jeff Zhao
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Huan He
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Yi Han
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Yun Feng
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Yuechen Jiang
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Yupeng Cao
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Haohang Li
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Yangyang Yu
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Xiaoyu Wang
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Penglei Gao
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Shengyuan Lin
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Keyi Wang
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Shanshan Yang
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Yilun Zhao
claimed_verified
2025-06-18T10:58:59.111Z
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Yilun Zhao
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Zhiwei Liu
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Peng Lu
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Jerry Huang
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Suyuchen Wang
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2025-06-18T16:15:35.878Z
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Suyuchen Wang
user
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Triantafillos Papadopoulos
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Polydoros Giannouris
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Efstathia Soufleri
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Nuo Chen
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Guojun Xiong
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Zhiyang Deng
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Yijia Zhao
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Mingquan Lin
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Meikang Qiu
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Kaleb E Smith
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Arman Cohan
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Xiao-Yang Liu
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Jimin Huang
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2025-06-19T09:10:29.859Z
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Jimin Huang
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Alejandro Lopez-Lira
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Xi Chen
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Junichi Tsujii
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Jian-Yun Nie
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Sophia Ananiadou
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Qianqian Xie
MultiFinBen: A Multilingual, Multimodal, and Difficulty-Aware Benchmark
  for Financial LLM Evaluation
Recent advances in large language models (LLMs) have accelerated progress in
financial NLP and applications, yet existing benchmarks remain limited to
monolingual and unimodal settings, often over-relying on simple tasks and
failing to reflect the complexity of real-world financial communication. We
introduce MultiFinBen, the first multilingual and multimodal benchmark tailored
to the global financial domain, evaluating LLMs across modalities (text,
vision, audio) and linguistic settings (monolingual, bilingual, multilingual)
on domain-specific tasks. We introduce two novel tasks, including PolyFiQA-Easy
and PolyFiQA-Expert, the first multilingual financial benchmarks requiring
models to perform complex reasoning over mixed-language inputs; and EnglishOCR
and SpanishOCR, the first OCR-embedded financial QA tasks challenging models to
extract and reason over information from visual-text financial documents.
Moreover, we propose a dynamic, difficulty-aware selection mechanism and curate
a compact, balanced benchmark rather than simple aggregation existing datasets.
Extensive evaluation of 22 state-of-the-art models reveals that even the
strongest models, despite their general multimodal and multilingual
capabilities, struggle dramatically when faced with complex cross-lingual and
multimodal tasks in financial domain. MultiFinBen is publicly released to
foster transparent, reproducible, and inclusive progress in financial studies
and applications.
MultiFinBen is a multilingual and multimodal benchmark for financial domain tasks, evaluating LLMs across modalities and linguistic settings, revealing challenges in complex cross-lingual and multimodal financial reasoning.
LLMs financial NLP multilingual multimodal benchmark domain-specific tasks PolyFiQA-Easy PolyFiQA-Expert EnglishOCR SpanishOCR dynamic selection mechanism difficulty-aware OCR-embedded financial QA