Tabular data is a fundamental component of real-world information systems. However, existing multilingual table benchmarks suffer from geolinguistic imbalance - overrepresenting certain languages and lacking sufficient scale for rigorous cross-lingual analysis. To address these limitations, we introduce M3TQA, which is a comprehensive framework for massively multilingual multitask table question answering, including subsequent datasets M3TQA-BENCH and M3TQA-INSTRUCT, featuring tables expanded to 97 languages from Chinese and English sources. M3TQA-BENCH includes 6,606 professionally annotated question-answering pairs across four tasks designed to evaluate nuanced table reasoning capabilities. Additionally, we synthesized the training set M3TQA-INSTRUCT in 97 languages using Large Language Model (LLM). Experiments on state-of-the-art LLMs reveal critical insights into cross-lingual generalization, demonstrating that synthetically generated, unannotated training data can significantly boost performance, particularly for low-resource languages. M3TQA establishes a new standard for multilingual table understanding, providing both a challenging evaluation platform and a scalable methodology for future research.

Code large language models (LLMs) have made significant progress in code debugging by directly generating the correct code based on the buggy code snippet. Programming benchmarks, typically consisting of buggy code snippets and their associated test cases, are used to assess the debugging capabilities of LLMs. However, many existing benchmarks primarily focus on Python and are often limited in terms of language diversity (e.g., DebugBench and DebugEval). To advancethe field of multilingual debugging with LLMs, we propose the first massively multilingual debugging benchmark, which includes 3.9K test samples of 20 programming languages and covers the automated program repair (APR) task, the bug localization(BL) task, and the bug identification (BI) task. In addition, we introduce the debugging instruction corpora MdEval-Instruct by injecting bugs into the correct multilingual queries and solutions (xDebugGen). Further, a multilingual debugger xDebugCoder trained on MdEval-Instruct as a strong baseline specifically to handle bugs of a wide range of programming languages (e.g. “Missing Mut” in language Rust and “Misused Macro Definition” in language C). Our extensive experiments on MdEval reveal a notable performance gap between open-source and closed-source LLMs (e.g., GPT and Claudeseries), highlighting huge room for improvement in multilingual code debugging scenarios.