@inproceedings{xu-etal-2025-cruxeval,
title = "{CRUXEVAL}-{X}: A Benchmark for Multilingual Code Reasoning, Understanding and Execution",
author = "Xu, Ruiyang and
Cao, Jialun and
Lu, Yaojie and
Wen, Ming and
Lin, Hongyu and
Han, Xianpei and
He, Ben and
Cheung, Shing-Chi and
Sun, Le",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1158/",
doi = "10.18653/v1/2025.acl-long.1158",
pages = "23762--23779",
ISBN = "979-8-89176-251-0",
abstract = "Code benchmarks such as HumanEval are widely adopted to evaluate Large Language Models' (LLMs) coding capabilities. However, there is an unignorable programming language bias in existing code benchmarks {--} over 95{\%} code generation benchmarks are dominated by Python, leaving the LLMs' capabilities in other programming languages such as Java and C/C++ unknown. Moreover, coding task bias is also crucial. Most benchmarks focus on code generation capability, while benchmarks for code reasoning (given input, reasoning output; and given output, reasoning input), an essential coding capability, are insufficient. Yet, constructing multi-lingual benchmarks can be expensive and labor-intensive, and codes in contest websites such as Leetcode suffer from data contamination during training. To fill this gap, we propose CRUXEVAL-X, a multi-lingual code reasoning benchmark that contains 19 programming languages. It comprises at least 600 subjects for each language, along with 19K content-consistent tests in total. In particular, the construction pipeline of CRUXEVAL-X works in a fully automated and test-guided manner, which iteratively generates and repairs based on execution feedback. Also, to cross language barriers (e.g., dynamic/static type systems in Python/C++), we formulated various transition rules between language pairs to facilitate translation. Our intensive evaluation of 24 representative LLMs reveals the correlation between language pairs. For example, TypeScript and JavaScript show a significant positive correlation, while Racket has less correlation with other languages. More interestingly, even a model trained solely on Python can achieve at most 34.4{\%} Pass@1 in other languages, revealing the cross-language generalization of LLMs."
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<abstract>Code benchmarks such as HumanEval are widely adopted to evaluate Large Language Models’ (LLMs) coding capabilities. However, there is an unignorable programming language bias in existing code benchmarks – over 95% code generation benchmarks are dominated by Python, leaving the LLMs’ capabilities in other programming languages such as Java and C/C++ unknown. Moreover, coding task bias is also crucial. Most benchmarks focus on code generation capability, while benchmarks for code reasoning (given input, reasoning output; and given output, reasoning input), an essential coding capability, are insufficient. Yet, constructing multi-lingual benchmarks can be expensive and labor-intensive, and codes in contest websites such as Leetcode suffer from data contamination during training. To fill this gap, we propose CRUXEVAL-X, a multi-lingual code reasoning benchmark that contains 19 programming languages. It comprises at least 600 subjects for each language, along with 19K content-consistent tests in total. In particular, the construction pipeline of CRUXEVAL-X works in a fully automated and test-guided manner, which iteratively generates and repairs based on execution feedback. Also, to cross language barriers (e.g., dynamic/static type systems in Python/C++), we formulated various transition rules between language pairs to facilitate translation. Our intensive evaluation of 24 representative LLMs reveals the correlation between language pairs. For example, TypeScript and JavaScript show a significant positive correlation, while Racket has less correlation with other languages. More interestingly, even a model trained solely on Python can achieve at most 34.4% Pass@1 in other languages, revealing the cross-language generalization of LLMs.</abstract>
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%0 Conference Proceedings
%T CRUXEVAL-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution
%A Xu, Ruiyang
%A Cao, Jialun
%A Lu, Yaojie
%A Wen, Ming
%A Lin, Hongyu
%A Han, Xianpei
%A He, Ben
%A Cheung, Shing-Chi
%A Sun, Le
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F xu-etal-2025-cruxeval
%X Code benchmarks such as HumanEval are widely adopted to evaluate Large Language Models’ (LLMs) coding capabilities. However, there is an unignorable programming language bias in existing code benchmarks – over 95% code generation benchmarks are dominated by Python, leaving the LLMs’ capabilities in other programming languages such as Java and C/C++ unknown. Moreover, coding task bias is also crucial. Most benchmarks focus on code generation capability, while benchmarks for code reasoning (given input, reasoning output; and given output, reasoning input), an essential coding capability, are insufficient. Yet, constructing multi-lingual benchmarks can be expensive and labor-intensive, and codes in contest websites such as Leetcode suffer from data contamination during training. To fill this gap, we propose CRUXEVAL-X, a multi-lingual code reasoning benchmark that contains 19 programming languages. It comprises at least 600 subjects for each language, along with 19K content-consistent tests in total. In particular, the construction pipeline of CRUXEVAL-X works in a fully automated and test-guided manner, which iteratively generates and repairs based on execution feedback. Also, to cross language barriers (e.g., dynamic/static type systems in Python/C++), we formulated various transition rules between language pairs to facilitate translation. Our intensive evaluation of 24 representative LLMs reveals the correlation between language pairs. For example, TypeScript and JavaScript show a significant positive correlation, while Racket has less correlation with other languages. More interestingly, even a model trained solely on Python can achieve at most 34.4% Pass@1 in other languages, revealing the cross-language generalization of LLMs.
%R 10.18653/v1/2025.acl-long.1158
%U https://aclanthology.org/2025.acl-long.1158/
%U https://doi.org/10.18653/v1/2025.acl-long.1158
%P 23762-23779
Markdown (Informal)
[CRUXEVAL-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution](https://aclanthology.org/2025.acl-long.1158/) (Xu et al., ACL 2025)
ACL
- Ruiyang Xu, Jialun Cao, Yaojie Lu, Ming Wen, Hongyu Lin, Xianpei Han, Ben He, Shing-Chi Cheung, and Le Sun. 2025. CRUXEVAL-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 23762–23779, Vienna, Austria. Association for Computational Linguistics.