Papers by Hualei Zhu

2 papers
MdEval: Massively Multilingual Code Debugging (2026.findings-acl)

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Challenge: Existing benchmarks primarily focus on Python and are limited in terms of language diversity.
Approach: They propose a multilingual debugging benchmark that includes 3.9K test samples of 20 programming languages and introduces the debug instruction corpora MdEval-Instruct by injecting bugs into the correct multilingual queries and solutions.
Outcome: The proposed benchmark includes 3.9K test samples of 20 programming languages and covers the automated program repair task, bug localization task, and bug identification task.
M3TQA: Massively Multilingual Multitask Table Question Answering (2026.findings-acl)

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Challenge: Existing multilingual table benchmarks suffer from geolinguistic imbalance - overrepresenting certain languages and lacking sufficient scale for rigorous cross-lingual analysis.
Approach: They propose a framework for massively multilingual table question answering that includes tables expanded to 97 languages from Chinese and English sources.
Outcome: Experiments on state-of-the-art LLMs show that synthetically generated training data significantly boosts performance, especially for low-resource languages.

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