@inproceedings{zhao-etal-2025-codejudge,
title = "{C}ode{J}udge-Eval: Can Large Language Models be Good Judges in Code Understanding?",
author = "Zhao, Yuwei and
Luo, Ziyang and
Tian, Yuchen and
Lin, Hongzhan and
Yan, Weixiang and
Li, Annan and
Ma, Jing",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.7/",
pages = "73--95",
abstract = "Recent advancements in large language models (LLMs) have showcased impressive code generation capabilities, primarily evaluated through language-to-code benchmarks. However, these benchmarks may not fully capture a model`s code understanding abilities. We introduce CodeJudge-Eval (CJ-Eval), a novel benchmark designed to assess LLMs' code understanding abilities from the perspective of code judging rather than code generation. CJ-Eval challenges models to determine the correctness of provided code solutions, encompassing various error types and compilation issues. By leveraging a diverse set of problems and a fine-grained judging system, CJ-Eval addresses the limitations of traditional benchmarks, including the potential memorization of solutions. Evaluation of 12 well-known LLMs on CJ-Eval reveals that even state-of-the-art models struggle, highlighting the benchmark`s ability to probe deeper into models' code understanding abilities. Our benchmark is available at https://github.com/CodeLLM-Research/CodeJudge-Eval ."
}
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<abstract>Recent advancements in large language models (LLMs) have showcased impressive code generation capabilities, primarily evaluated through language-to-code benchmarks. However, these benchmarks may not fully capture a model‘s code understanding abilities. We introduce CodeJudge-Eval (CJ-Eval), a novel benchmark designed to assess LLMs’ code understanding abilities from the perspective of code judging rather than code generation. CJ-Eval challenges models to determine the correctness of provided code solutions, encompassing various error types and compilation issues. By leveraging a diverse set of problems and a fine-grained judging system, CJ-Eval addresses the limitations of traditional benchmarks, including the potential memorization of solutions. Evaluation of 12 well-known LLMs on CJ-Eval reveals that even state-of-the-art models struggle, highlighting the benchmark‘s ability to probe deeper into models’ code understanding abilities. Our benchmark is available at https://github.com/CodeLLM-Research/CodeJudge-Eval .</abstract>
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%0 Conference Proceedings
%T CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding?
%A Zhao, Yuwei
%A Luo, Ziyang
%A Tian, Yuchen
%A Lin, Hongzhan
%A Yan, Weixiang
%A Li, Annan
%A Ma, Jing
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F zhao-etal-2025-codejudge
%X Recent advancements in large language models (LLMs) have showcased impressive code generation capabilities, primarily evaluated through language-to-code benchmarks. However, these benchmarks may not fully capture a model‘s code understanding abilities. We introduce CodeJudge-Eval (CJ-Eval), a novel benchmark designed to assess LLMs’ code understanding abilities from the perspective of code judging rather than code generation. CJ-Eval challenges models to determine the correctness of provided code solutions, encompassing various error types and compilation issues. By leveraging a diverse set of problems and a fine-grained judging system, CJ-Eval addresses the limitations of traditional benchmarks, including the potential memorization of solutions. Evaluation of 12 well-known LLMs on CJ-Eval reveals that even state-of-the-art models struggle, highlighting the benchmark‘s ability to probe deeper into models’ code understanding abilities. Our benchmark is available at https://github.com/CodeLLM-Research/CodeJudge-Eval .
%U https://aclanthology.org/2025.coling-main.7/
%P 73-95
Markdown (Informal)
[CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding?](https://aclanthology.org/2025.coling-main.7/) (Zhao et al., COLING 2025)
ACL