# Overview of FinMMEval 2026 Task 1: Multilingual Financial Multiple-Choice Question Answering Source: [Overview of FinMMEval 2026 Task 1: Multilingual Financial Multiple-Choice Question Answering]() Collection index: [llms.txt]() Data updated: 2026-07-24T05:00:05.476Z Authors: Zhuohan Xie, Yuyang Dai, Rania Elbadry, Vanshikaa Jani, Georgi Georgiev, Dimitar Dimitrov, Fan Zhang, Xueqing Peng, Lingfei Qian, Jimin Huang, Jiahui Geng, Yankai Chen, Ye Yuan, Haolun Wu, Yuxia Wang, Ivan Koychev, Veselin Stoyanov, Mingzi Song, Yu Chen, Xue Liu, Preslav Nakov Published: 2026-07-22T07:43:44.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()