@inproceedings{lou-etal-2026-unifineval,
title = "{U}ni{F}in{E}val: Towards Unified Evaluation of Financial Multimodal Models across Text, Images and Videos",
author = "Lou, Fangqi and
Yang, Zhi and
Zeng, Lingfeng and
Jin, Zhiheng and
Qi, Qi and
Zhang, Wei and
Wu, Zhenyu and
Yu, Zhenxiong and
Zhang, Lejie and
Huang, Xiaoming and
Liang, Xiaolong and
Wei, Zheng and
Zou, Junbo and
Cheng, Dongpo and
Liu, Zhaowei and
Guo, Xin and
Zhang, Rongjunchen and
Zhang, Liwen",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2075/",
pages = "44168--44206",
ISBN = "979-8-89176-462-0",
abstract = "Multimodal large language models (MLLMs) have shown increasing potential for applications in the financial domain. However, the challenges posed by real-world financial scenarios, including high-information-density, strong noise interference, and cross-modal multi-hop reasoning, go beyond the evaluation scope of existing financial multimodal benchmarks. To this end, we propose UniFinEval, the first unified multimodal financial evaluation benchmark designed for high-density financial information environments, covering three modalities: text, image, and video. UniFinEval abstracts three progressive core scenarios from perception to reasoning and decision-making, and further refines them into six task categories. It contains 5,567 high-quality Chinese and English question{--}answer (QA) pairs constructed by financial experts. We systematically evaluate 10 mainstream MLLMs under zero-shot and chain-of-thought (CoT) settings. Experimental results show that Gemini-3-Pro achieves the best overall performance, but still exhibits a significant gap compared with financial experts. Further error analysis reveals the capability deficiencies of MLLMs in financial scenarios. UniFinEval aims to systematically evaluate the capabilities of MLLMs in real financial scenarios, thereby promoting the application of MLLMs in the financial domain. The related code and data are available at https://github.com/aifinlab/UniFinEval."
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<abstract>Multimodal large language models (MLLMs) have shown increasing potential for applications in the financial domain. However, the challenges posed by real-world financial scenarios, including high-information-density, strong noise interference, and cross-modal multi-hop reasoning, go beyond the evaluation scope of existing financial multimodal benchmarks. To this end, we propose UniFinEval, the first unified multimodal financial evaluation benchmark designed for high-density financial information environments, covering three modalities: text, image, and video. UniFinEval abstracts three progressive core scenarios from perception to reasoning and decision-making, and further refines them into six task categories. It contains 5,567 high-quality Chinese and English question–answer (QA) pairs constructed by financial experts. We systematically evaluate 10 mainstream MLLMs under zero-shot and chain-of-thought (CoT) settings. Experimental results show that Gemini-3-Pro achieves the best overall performance, but still exhibits a significant gap compared with financial experts. Further error analysis reveals the capability deficiencies of MLLMs in financial scenarios. UniFinEval aims to systematically evaluate the capabilities of MLLMs in real financial scenarios, thereby promoting the application of MLLMs in the financial domain. The related code and data are available at https://github.com/aifinlab/UniFinEval.</abstract>
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%0 Conference Proceedings
%T UniFinEval: Towards Unified Evaluation of Financial Multimodal Models across Text, Images and Videos
%A Lou, Fangqi
%A Yang, Zhi
%A Zeng, Lingfeng
%A Jin, Zhiheng
%A Qi, Qi
%A Zhang, Wei
%A Wu, Zhenyu
%A Yu, Zhenxiong
%A Zhang, Lejie
%A Huang, Xiaoming
%A Liang, Xiaolong
%A Wei, Zheng
%A Zou, Junbo
%A Cheng, Dongpo
%A Liu, Zhaowei
%A Guo, Xin
%A Zhang, Rongjunchen
%A Zhang, Liwen
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F lou-etal-2026-unifineval
%X Multimodal large language models (MLLMs) have shown increasing potential for applications in the financial domain. However, the challenges posed by real-world financial scenarios, including high-information-density, strong noise interference, and cross-modal multi-hop reasoning, go beyond the evaluation scope of existing financial multimodal benchmarks. To this end, we propose UniFinEval, the first unified multimodal financial evaluation benchmark designed for high-density financial information environments, covering three modalities: text, image, and video. UniFinEval abstracts three progressive core scenarios from perception to reasoning and decision-making, and further refines them into six task categories. It contains 5,567 high-quality Chinese and English question–answer (QA) pairs constructed by financial experts. We systematically evaluate 10 mainstream MLLMs under zero-shot and chain-of-thought (CoT) settings. Experimental results show that Gemini-3-Pro achieves the best overall performance, but still exhibits a significant gap compared with financial experts. Further error analysis reveals the capability deficiencies of MLLMs in financial scenarios. UniFinEval aims to systematically evaluate the capabilities of MLLMs in real financial scenarios, thereby promoting the application of MLLMs in the financial domain. The related code and data are available at https://github.com/aifinlab/UniFinEval.
%U https://aclanthology.org/2026.findings-emnlp.2075/
%P 44168-44206
Markdown (Informal)
[UniFinEval: Towards Unified Evaluation of Financial Multimodal Models across Text, Images and Videos](https://aclanthology.org/2026.findings-emnlp.2075/) (Lou et al., Findings 2026)
ACL
- Fangqi Lou, Zhi Yang, Lingfeng Zeng, Zhiheng Jin, Qi Qi, Wei Zhang, Zhenyu Wu, Zhenxiong Yu, Lejie Zhang, Xiaoming Huang, Xiaolong Liang, Zheng Wei, Junbo Zou, Dongpo Cheng, Zhaowei Liu, Xin Guo, Rongjunchen Zhang, and Liwen Zhang. 2026. UniFinEval: Towards Unified Evaluation of Financial Multimodal Models across Text, Images and Videos. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 44168–44206, Budapest, Hungary. Association for Computational Linguistics.