The CLEF-2026 FinMMEval Lab: Multilingual and Multimodal Evaluation of Financial AI Systems

TL;DR

The FinMMEval Lab at CLEF 2026 introduces a comprehensive multilingual and multimodal evaluation framework for financial AI systems, addressing current limitations in monolingual, text-only benchmarks.

Contribution

It presents the first interconnected tasks for multilingual, multimodal financial understanding, reasoning, and decision-making, with publicly available datasets and evaluation tools.

Findings

New benchmark for multilingual financial NLP tasks

Evaluation of reasoning and decision-making in financial AI

Promotion of inclusive and robust financial AI systems

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.