首个系统评估大模型汇编转源码的基准，揭示性能与效率权衡。
Asm2SrcEval: Evaluating Large Language Models for Assembly-to-Source Code Translation
- 构建多维度评估体系，涵盖语义、流畅性与效率。
- 五款模型在相似度与推理速度间存在明显性能差异。
- 发现控制流恢复和变量名重建是主要难点，适合逆向工程研究者参考。
汇编到源码的翻译在逆向工程、网络安全和软件维护中至关重要，但针对大语言模型在此任务上的系统性评估仍十分稀缺。本文首次全面评估了五种前沿大语言模型在汇编转源码任务上的表现。采用多种指标：词汇相似度（BLEU、ROUGE、METEOR）、语义对齐（BERTScore）、流畅性（困惑度）及效率（预测时间）。结果表明存在明显权衡：某些模型在文本相似度上表现优异，而另一些则具有更低的困惑度或更快的推理速度。我们进一步进行了定性分析，揭示典型成功与失败案例，指出控制流恢复和标识符重构仍是主要挑战。整体而言，本基准为理解当前大模型在程序翻译中的优劣提供了可操作洞见，为未来兼顾准确率与效率的研究奠定基础。
原文摘要 · Abstract (English)
Assembly-to-source code translation is a critical task in reverse engineering, cybersecurity, and software maintenance, yet systematic benchmarks for evaluating large language models on this problem remain scarce. In this work, we present the first comprehensive evaluation of five state-of-the-art large language models on assembly-to-source translation. We assess model performance using a diverse set of metrics capturing lexical similarity (BLEU, ROUGE, and METEOR), semantic alignment (BERTScore), fluency (Perplexity), and efficiency (time prediction). Our results reveal clear trade-offs: while certain models excel in text similarity metrics, others demonstrate lower perplexity or faster inference times. We further provide qualitative analyses of typical model successes and failure cases, highlighting challenges such as control flow recovery and identifier reconstruction. Taken together, our benchmark offers actionable insights into the strengths and limitations of current large language models for program translation, establishing a foundation for future research in combining accuracy with efficiency for real-world applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。