MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application
Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
Correct Metadata for
Abstract
Real-world financial analysis involves information across multiple languages and modalities, from reports and news to scanned filings and meeting recordings. Yet most existing evaluations of LLMs in finance remain text-only, monolingual, and largely saturated by current models. To bridge these gaps, we present MultiFinBen, the first expert-annotated multilingual (five languages) and multimodal (text, vision, audio) benchmark for evaluating LLMs in realistic financial contexts. MultiFinBen introduces two new task families: multilingual financial reasoning, which tests cross-lingual evidence integration from filings and news, and financial OCR, which extracts structured text from scanned documents containing tables and charts. Rather than aggregating all available datasets, we apply a structured, difficulty-aware selection based on advanced model performance, ensuring balanced challenge and removing redundant tasks. 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. These findings expose persistent limitations in multilingual, multimodal, and expert-level financial reasoning. All datasets, evaluation scripts, and leaderboards are publicly released.- Anthology ID:
- 2026.acl-long.770
- Volume:
- Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2026
- Address:
- San Diego, California, United States
- Editors:
- Maria Liakata, Viviane P. Moreira, Jiajun Zhang, David Jurgens
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 16934–16963
- Language:
- URL:
- https://aclanthology.org/2026.acl-long.770/
- DOI:
- Bibkey:
- Cite (ACL):
- Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, and Qianqian Xie. 2026. MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16934–16963, San Diego, California, United States. Association for Computational Linguistics.
- Cite (Informal):
- MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (Peng et al., ACL 2026)
- Copy Citation:
- PDF:
- https://aclanthology.org/2026.acl-long.770.pdf
- Checklist:
- 2026.acl-long.770.checklist.pdf
Export citation
@inproceedings{peng-etal-2026-multifinben,
title = "{M}ulti{F}in{B}en: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application",
author = "Peng, Xueqing and
Qian, Lingfei and
Wang, Yan and
Xiang, Ruoyu and
He, Yueru and
Ren, Yang and
Jiang, Mingyang and
Zhang, Vincent Jim and
Guo, Yuqing and
Zhao, Jeff and
He, Huan and
Han, Yi and
Feng, Yun and
Jiang, Yuechen and
Cao, Yupeng and
Li, Haohang and
Yu, Yangyang and
Wang, Xiaoyu and
Gao, Penglei and
Lin, Shengyuan and
Wang, Keyi and
Yang, Shanshan and
Zhao, Yilun and
Liu, Zhiwei and
Lu, Peng and
Huang, Jerry and
Wang, Suyuchen and
Papadopoulos, Triantafillos and
Giannouris, Polydoros and
Soufleri, Efstathia and
Chen, Nuo and
Deng, Zhiyang and
Fu, Heming and
Zhao, Yijia and
Lin, Mingquan and
Qiu, Meikang and
Smith, Kaleb E and
Cohan, Arman and
Liu, Xiao-Yang and
Huang, Jimin and
Xiong, Guojun and
Lopez-Lira, Alejandro and
Chen, Xi and
Tsujii, Junichi and
Nie, Jian-Yun and
Ananiadou, Sophia and
Xie, Qianqian",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.770/",
pages = "16934--16963",
ISBN = "979-8-89176-390-6",
abstract = "Real-world financial analysis involves information across multiple languages and modalities, from reports and news to scanned filings and meeting recordings. Yet most existing evaluations of LLMs in finance remain text-only, monolingual, and largely saturated by current models. To bridge these gaps, we present MultiFinBen, the first expert-annotated multilingual (five languages) and multimodal (text, vision, audio) benchmark for evaluating LLMs in realistic financial contexts. MultiFinBen introduces two new task families: multilingual financial reasoning, which tests cross-lingual evidence integration from filings and news, and financial OCR, which extracts structured text from scanned documents containing tables and charts. Rather than aggregating all available datasets, we apply a structured, difficulty-aware selection based on advanced model performance, ensuring balanced challenge and removing redundant tasks. 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. These findings expose persistent limitations in multilingual, multimodal, and expert-level financial reasoning. All datasets, evaluation scripts, and leaderboards are publicly released."
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<abstract>Real-world financial analysis involves information across multiple languages and modalities, from reports and news to scanned filings and meeting recordings. Yet most existing evaluations of LLMs in finance remain text-only, monolingual, and largely saturated by current models. To bridge these gaps, we present MultiFinBen, the first expert-annotated multilingual (five languages) and multimodal (text, vision, audio) benchmark for evaluating LLMs in realistic financial contexts. MultiFinBen introduces two new task families: multilingual financial reasoning, which tests cross-lingual evidence integration from filings and news, and financial OCR, which extracts structured text from scanned documents containing tables and charts. Rather than aggregating all available datasets, we apply a structured, difficulty-aware selection based on advanced model performance, ensuring balanced challenge and removing redundant tasks. 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. These findings expose persistent limitations in multilingual, multimodal, and expert-level financial reasoning. All datasets, evaluation scripts, and leaderboards are publicly released.</abstract>
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%0 Conference Proceedings %T MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application %A Peng, Xueqing %A Qian, Lingfei %A Wang, Yan %A Xiang, Ruoyu %A He, Yueru %A Ren, Yang %A Jiang, Mingyang %A Zhang, Vincent Jim %A Guo, Yuqing %A Zhao, Jeff %A He, Huan %A Han, Yi %A Feng, Yun %A Jiang, Yuechen %A Cao, Yupeng %A Li, Haohang %A Yu, Yangyang %A Wang, Xiaoyu %A Gao, Penglei %A Lin, Shengyuan %A Wang, Keyi %A Yang, Shanshan %A Zhao, Yilun %A Liu, Zhiwei %A Lu, Peng %A Huang, Jerry %A Wang, Suyuchen %A Papadopoulos, Triantafillos %A Giannouris, Polydoros %A Soufleri, Efstathia %A Chen, Nuo %A Deng, Zhiyang %A Fu, Heming %A Zhao, Yijia %A Lin, Mingquan %A Qiu, Meikang %A Smith, Kaleb E. %A Cohan, Arman %A Liu, Xiao-Yang %A Huang, Jimin %A Xiong, Guojun %A Lopez-Lira, Alejandro %A Chen, Xi %A Tsujii, Junichi %A Nie, Jian-Yun %A Ananiadou, Sophia %A Xie, Qianqian %Y Liakata, Maria %Y Moreira, Viviane P. %Y Zhang, Jiajun %Y Jurgens, David %S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) %D 2026 %8 July %I Association for Computational Linguistics %C San Diego, California, United States %@ 979-8-89176-390-6 %F peng-etal-2026-multifinben %X Real-world financial analysis involves information across multiple languages and modalities, from reports and news to scanned filings and meeting recordings. Yet most existing evaluations of LLMs in finance remain text-only, monolingual, and largely saturated by current models. To bridge these gaps, we present MultiFinBen, the first expert-annotated multilingual (five languages) and multimodal (text, vision, audio) benchmark for evaluating LLMs in realistic financial contexts. MultiFinBen introduces two new task families: multilingual financial reasoning, which tests cross-lingual evidence integration from filings and news, and financial OCR, which extracts structured text from scanned documents containing tables and charts. Rather than aggregating all available datasets, we apply a structured, difficulty-aware selection based on advanced model performance, ensuring balanced challenge and removing redundant tasks. 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. These findings expose persistent limitations in multilingual, multimodal, and expert-level financial reasoning. All datasets, evaluation scripts, and leaderboards are publicly released. %U https://aclanthology.org/2026.acl-long.770/ %P 16934-16963
Markdown (Informal)
[MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application](https://aclanthology.org/2026.acl-long.770/) (Peng et al., ACL 2026)
- MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (Peng et al., ACL 2026)
ACL
- Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, and Qianqian Xie. 2026. MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16934–16963, San Diego, California, United States. Association for Computational Linguistics.