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
Xie, Zhuohan
Elbadry, Rania
Zhang, Fan
Georgiev, Georgi
Peng, Xueqing
Qian, Lingfei
Huang, Jimin
Dimitrov, Dimitar
Jani, Vanshikaa
Dai, Yuyang
Supervisor
Department
Natural Language Processing
Embargo End Date
Type
Conference proceeding
Date
License
Language
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
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.
Citation
Z. Xie, R. Elbadry, F. Zhang, G. Georgiev, X. Peng, L. Qian , et al., "The CLEF-2026 FinMMEval Lab: Multilingual and Multimodal Evaluation of Financial AI Systems," 2026, pp. 267-276.
Source
Lecture Notes in Computer Science
Conference
48th European Conference on Information Retrieval, ECIR 2026
Keywords
46 Information and Computing Sciences, 4602 Artificial Intelligence, 4605 Data Management and Data Science
Subjects
Source
48th European Conference on Information Retrieval, ECIR 2026
Publisher
Springer Nature