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arXiv AI By Hui Sun, Anderson Uch\^oa, Rohit Gheyi, Wesley K. G. Assun\c{c}\~ao

Evaluating Language Models on Cross-Language Code Functional Equivalence

Read the original on arXiv AI →

The paper introduces PolyHuman, a dataset of human-written programs in C++, Java, and Python, to test whether large language models can judge functional equivalence across languages. Using this dataset, the authors evaluate several open-weight and proprietary LLMs, finding that models struggle more with harder problems, show language-specific biases, and rely partly on superficial similarity cues. They also observe run‑to‑run instability in GPT‑o4‑mini, concluding that current LLMs do not reliably capture functional equivalence within or across programming languages.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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