Overview of FinMMEval 2026 Task 1: Multilingual Financial Multiple-Choice Question Answering

Zhuohan Xie, Yuyang Dai, Rania Elbadry, Vanshikaa Jani, Georgi Georgiev +16 more
7/22/2026
cs.CLcs.AIcs.CE

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Abstract

FinMMEval 2026 Task 1 evaluates multilingual financial multiple-choice question answering in English, Chinese, Arabic, and Hindi. The task tests whether systems can select the correct answer to finance questions involving domain terminology, numerical interpretation, and conceptual financial reasoning across languages and scripts. The final-test set contains 800 questions, with 200 questions per language; gold answers were withheld during submission, and each language was ranked independently by accuracy. The final leaderboards contain 13 English, 11 Chinese, 11 Arabic, and 10 Hindi ranked submissions. Top accuracies range from 92.0% in Hindi to 97.5% in English and Arabic, with the same leading teams appearing near the top across all four languages. The documented systems used retrieval augmentation, direct answer-option scoring, language-specific prompting, selective self-consistency, confidence checks, and LLM-based review stages.

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Cite this paper

@article{xie2026overview,
  title  = {Overview of FinMMEval 2026 Task 1: Multilingual Financial Multiple-Choice Question Answering},
  author = {Zhuohan Xie and Yuyang Dai and Rania Elbadry and Vanshikaa Jani and Georgi Georgiev and Dimitar Dimitrov and Fan Zhang and Xueqing Peng and Lingfei Qian and Jimin Huang and Jiahui Geng and Yankai Chen and Ye Yuan and Haolun Wu and Yuxia Wang and Ivan Koychev and Veselin Stoyanov and Mingzi Song and Yu Chen and Xue Liu and Preslav Nakov},
  year   = {2026},
  eprint = {2607.19856},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2607.19856v1}
}

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