@inproceedings{luo-etal-2025-finmme,
title = "{F}in{MME}: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation",
author = "Luo, Junyu and
Kou, Zhizhuo and
Yang, Liming and
Luo, Xiao and
Huang, Jinsheng and
Xiao, Zhiping and
Peng, Jingshu and
Liu, Chengzhong and
Ji, Jiaming and
Liu, Xuanzhe and
Han, Sirui and
Zhang, Ming and
Guo, Yike",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1426/",
doi = "10.18653/v1/2025.acl-long.1426",
pages = "29465--29489",
ISBN = "979-8-89176-251-0",
abstract = "Multimodal Large Language Models (MLLMs) have experienced rapid development in recent years. However, in the financial domain, there is a notable lack of effective and specialized multimodal evaluation datasets. To advance the development of MLLMs in the finance domain, we introduce FinMME, encompassing more than 11,000 high-quality financial research samples across 18 financial domains and 6 asset classes, featuring 10 major chart types and 21 subtypes. We ensure data quality through 20 annotators and carefully designed validation mechanisms. Additionally, we develop FinScore, an evaluation system incorporating hallucination penalties and multi-dimensional capability assessment to provide an unbiased evaluation. Extensive experimental results demonstrate that even state-of-the-art models like GPT-4o exhibit unsatisfactory performance on FinMME, highlighting its challenging nature. The benchmark exhibits high robustness with prediction variations under different prompts remaining below 1{\%}, demonstrating superior reliability compared to existing datasets. Our dataset and evaluation protocol are available at https://huggingface.co/datasets/luojunyu/FinMME and https://github.com/luo-junyu/FinMME."
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<abstract>Multimodal Large Language Models (MLLMs) have experienced rapid development in recent years. However, in the financial domain, there is a notable lack of effective and specialized multimodal evaluation datasets. To advance the development of MLLMs in the finance domain, we introduce FinMME, encompassing more than 11,000 high-quality financial research samples across 18 financial domains and 6 asset classes, featuring 10 major chart types and 21 subtypes. We ensure data quality through 20 annotators and carefully designed validation mechanisms. Additionally, we develop FinScore, an evaluation system incorporating hallucination penalties and multi-dimensional capability assessment to provide an unbiased evaluation. Extensive experimental results demonstrate that even state-of-the-art models like GPT-4o exhibit unsatisfactory performance on FinMME, highlighting its challenging nature. The benchmark exhibits high robustness with prediction variations under different prompts remaining below 1%, demonstrating superior reliability compared to existing datasets. Our dataset and evaluation protocol are available at https://huggingface.co/datasets/luojunyu/FinMME and https://github.com/luo-junyu/FinMME.</abstract>
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%0 Conference Proceedings
%T FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation
%A Luo, Junyu
%A Kou, Zhizhuo
%A Yang, Liming
%A Luo, Xiao
%A Huang, Jinsheng
%A Xiao, Zhiping
%A Peng, Jingshu
%A Liu, Chengzhong
%A Ji, Jiaming
%A Liu, Xuanzhe
%A Han, Sirui
%A Zhang, Ming
%A Guo, Yike
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F luo-etal-2025-finmme
%X Multimodal Large Language Models (MLLMs) have experienced rapid development in recent years. However, in the financial domain, there is a notable lack of effective and specialized multimodal evaluation datasets. To advance the development of MLLMs in the finance domain, we introduce FinMME, encompassing more than 11,000 high-quality financial research samples across 18 financial domains and 6 asset classes, featuring 10 major chart types and 21 subtypes. We ensure data quality through 20 annotators and carefully designed validation mechanisms. Additionally, we develop FinScore, an evaluation system incorporating hallucination penalties and multi-dimensional capability assessment to provide an unbiased evaluation. Extensive experimental results demonstrate that even state-of-the-art models like GPT-4o exhibit unsatisfactory performance on FinMME, highlighting its challenging nature. The benchmark exhibits high robustness with prediction variations under different prompts remaining below 1%, demonstrating superior reliability compared to existing datasets. Our dataset and evaluation protocol are available at https://huggingface.co/datasets/luojunyu/FinMME and https://github.com/luo-junyu/FinMME.
%R 10.18653/v1/2025.acl-long.1426
%U https://aclanthology.org/2025.acl-long.1426/
%U https://doi.org/10.18653/v1/2025.acl-long.1426
%P 29465-29489
Markdown (Informal)
[FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation](https://aclanthology.org/2025.acl-long.1426/) (Luo et al., ACL 2025)
ACL
- Junyu Luo, Zhizhuo Kou, Liming Yang, Xiao Luo, Jinsheng Huang, Zhiping Xiao, Jingshu Peng, Chengzhong Liu, Jiaming Ji, Xuanzhe Liu, Sirui Han, Ming Zhang, and Yike Guo. 2025. FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 29465–29489, Vienna, Austria. Association for Computational Linguistics.