The CLEF-2026 FinMMEval Lab: Multilingual and Multimodal Evaluation of Financial AI Systems
Zhuohan Xie, Rania Elbadry, Fan Zhang, Georgi Georgiev, Xueqing Peng, Lingfei Qian, Jimin Huang, Dimitar Dimitrov, Vanshikaa Jani, Yuyang Dai, Jiahui Geng, Yuxia Wang, Ivan Koychev, Veselin Stoyanov, Preslav Nakov
TL;DR
FinMMEval addresses the gap in financial NLP benchmarks by proposing a multilingual, multimodal evaluation framework for financial LLMs. It introduces three interlinked tasks—Task 1 Financial Exam Question Answering, Task 2 Multilingual Financial Question Answering (PolyFiQA), and Task 3 Financial Decision Making—that collectively assess knowledge, cross-lingual reasoning, and actionability across multiple languages and data modalities such as reports, news, and market signals. The paper details dataset construction, evaluation metrics, and live-environment constraints, with datasets and resources intended to be publicly released to support reproducibility. This framework aims to foster robust, transparent, and globally inclusive financial AI systems capable of reasoning across languages and modalities and making evidence-grounded, risk-aware decisions in real time.
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.