The CLEF-2026 FinMMEval Lab: Multilingual and Multimodal Evaluation of Financial AI Systems
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910019054731264 |
|---|---|
| author | Xie, Zhuohan Elbadry, Rania Zhang, Fan Georgiev, Georgi Peng, Xueqing Qian, Lingfei Huang, Jimin Dimitrov, Dimitar Jani, Vanshikaa Dai, Yuyang Geng, Jiahui Wang, Yuxia Koychev, Ivan Stoyanov, Veselin Nakov, Preslav |
| author_facet | Xie, Zhuohan Elbadry, Rania Zhang, Fan Georgiev, Georgi Peng, Xueqing Qian, Lingfei Huang, Jimin Dimitrov, Dimitar Jani, Vanshikaa Dai, Yuyang Geng, Jiahui Wang, Yuxia Koychev, Ivan Stoyanov, Veselin Nakov, Preslav |
| contents | We present the setup and the tasks of the FinMMEval Lab at CLEF 2026, which introduces the first multilingual and multimodal evaluation framework for financial Large Language Models (LLMs). While recent advances in financial natural language processing have enabled automated analysis of market reports, regulatory documents, and investor communications, existing benchmarks remain largely monolingual, text-only, and limited to narrow subtasks. FinMMEval 2026 addresses this gap by offering three interconnected tasks that span financial understanding, reasoning, and decision-making: Financial Exam Question Answering, Multilingual Financial Question Answering (PolyFiQA), and Financial Decision Making. Together, these tasks provide a comprehensive evaluation suite that measures models' ability to reason, generalize, and act across diverse languages and modalities. The lab aims to promote the development of robust, transparent, and globally inclusive financial AI systems, with datasets and evaluation resources publicly released to support reproducible research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_10886 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The CLEF-2026 FinMMEval Lab: Multilingual and Multimodal Evaluation of Financial AI Systems Xie, Zhuohan Elbadry, Rania Zhang, Fan Georgiev, Georgi Peng, Xueqing Qian, Lingfei Huang, Jimin Dimitrov, Dimitar Jani, Vanshikaa Dai, Yuyang Geng, Jiahui Wang, Yuxia Koychev, Ivan Stoyanov, Veselin Nakov, Preslav Computation and Language Artificial Intelligence Computational Engineering, Finance, and Science We present the setup and the tasks of the FinMMEval Lab at CLEF 2026, which introduces the first multilingual and multimodal evaluation framework for financial Large Language Models (LLMs). While recent advances in financial natural language processing have enabled automated analysis of market reports, regulatory documents, and investor communications, existing benchmarks remain largely monolingual, text-only, and limited to narrow subtasks. FinMMEval 2026 addresses this gap by offering three interconnected tasks that span financial understanding, reasoning, and decision-making: Financial Exam Question Answering, Multilingual Financial Question Answering (PolyFiQA), and Financial Decision Making. Together, these tasks provide a comprehensive evaluation suite that measures models' ability to reason, generalize, and act across diverse languages and modalities. The lab aims to promote the development of robust, transparent, and globally inclusive financial AI systems, with datasets and evaluation resources publicly released to support reproducible research. |
| title | The CLEF-2026 FinMMEval Lab: Multilingual and Multimodal Evaluation of Financial AI Systems |
| topic | Computation and Language Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2602.10886 |