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TransAgent: Enhancing LLM-Based Code Translation via Fine-Grained Execution Alignment

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学术前沿 6.4 分 — 有一定参考价值的AI研究论文
原文: cs.AI updates on arXiv.org

评分 6.4 · 来源:cs.AI updates on arXiv.org · 发布于 2026-04-08

评分依据:有一定参考价值的AI研究论文

arXiv:2409.19894v5 Announce Type: replace-cross Abstract: Code translation transforms code between programming languages while preserving functionality, which is critical in software development and maintenance. While traditional learning-based code translation methods have limited effectiveness due to the lack of sufficient parallel training data, Large Language Models (LLMs) have recently advanced this field with their strong code generation and comprehension capabilities. However, code translated by LLMs still suffers from diverse quality issues, such as syntax and semantic errors. In this


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