研究人员开发了一个知识蒸馏框架，以提高用于跨语言代码克隆检测的紧凑型开源模型的可靠性和实用性。该方法将推理能力从一个更大的模型DeepSeek-R1转移到Phi3和Qwen-Coder等更小的模型中。该方法结合了响应稳定技术，并利用了源自Project CodeNet的合成训练数据，显示出改进的性能和缩短的推理时间。 AI
影响 增强了小型开源模型在专门代码分析任务中的效用，可能减少对更大、专有系统的依赖。
排序理由 这是一篇研究论文，详细介绍了一种改进特定任务开源模型的新方法。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2605.02860v1 Announce Type: cross Abstract: Cross-language code clone detection (X-CCD) is challenging because semantically equivalent programs written in different languages often share little surface similarity. Although large language models (LLMs) have shown promise for…
Cross-language code clone detection (X-CCD) is challenging because semantically equivalent programs written in different languages often share little surface similarity. Although large language models (LLMs) have shown promise for semantic clone detection, their use as black-box …