标题： 通过层感知模型编辑提高代码LLM对提示扰动的鲁棒性 显示英文标题

标题： Improving Code LLM Robustness to Prompt Perturbations via Layer-Aware Model Editing

摘要： 大型语言模型（LLMs）在代码生成方面表现出色，其中自然语言提示在向模型传达用户意图方面起着关键作用。 然而，先前的研究表明，LLMs对提示扰动非常敏感。措辞、语法或格式的微小修改可能会显著降低生成代码的功能正确性。 由于扰动在现实场景中经常发生，提高LLMs对提示扰动的鲁棒性对于确保实际代码生成中的可靠性能至关重要。 在本文中，我们引入了CREME（通过模型编辑增强代码鲁棒性），这是一种通过有针对性的参数更新来增强LLMs鲁棒性的新方法。 CREME首先通过比较原始提示与其扰动变体之间的隐藏状态来识别鲁棒性敏感层。 然后，它在识别出的层上进行轻量级参数编辑，以减少性能下降。 我们在两个广泛使用的代码生成基准（HumanEval和MBPP）及其扰动版本上评估了CREME。 实验结果表明，CREME在扰动提示上的Pass@1准确率提高了63%，同时在干净输入上保持稳定性能，准确率偏差在1%以内。 进一步分析表明，鲁棒性敏感层主要集中在网络的中间和深层，且它们的位置在不同的模型架构中有所不同。 这些见解为开发未来的面向鲁棒性的编辑策略提供了有价值的基础。 显示英文摘要

摘要： Large language models (LLMs) have demonstrated impressive capabilities in code generation, where the natural language prompt plays a crucial role in conveying user intent to the model. However, prior studies have shown that LLMs are highly sensitive to prompt perturbations. Minor modifications in wording, syntax, or formatting can significantly reduce the functional correctness of generated code. As perturbations frequently occur in real-world scenarios, improving the robustness of LLMs to prompt perturbations is essential for ensuring reliable performance in practical code generation. In this paper, we introduce CREME (Code Robustness Enhancement via Model Editing), a novel approach that enhances LLM robustness through targeted parameter updates. CREME first identifies robustness-sensitive layers by comparing hidden states between an original prompt and its perturbed variant. Then, it performs lightweight parameter editing at the identified layer to reduce performance degradation. We evaluate CREME on two widely used code generation benchmarks (HumanEval and MBPP) along with their perturbed counterparts. Experimental results show that CREME improves Pass@1 accuracy by 63% on perturbed prompts while maintaining stable performance on clean inputs, with accuracy deviations within 1%. Further analysis reveals that robustness-sensitive layers are primarily concentrated in the middle and deeper layers of the network, and their locations vary across different model architectures. These insights provide a valuable foundation for developing future robustness-oriented editing strategies.