提升大模型对指令扰动的鲁棒性，发现自去噪效果优于其他方法。
Enhancing LLM Robustness to Perturbed Instructions: An Empirical Study
- 通过自去噪技术修复字符和词级指令扰动。
- 自去噪使模型平均性能显著提升，优于集成与监督方法。
- 适用于任务型与角色型指令，对多种模型和数据集有效。
大型语言模型（LLMs）对输入扰动极为敏感，微小的提示修改可能导致输出显著变化。现有方法主要针对数据样本扰动，而对任务级指令扰动的韧性提升仍研究不足。本文聚焦于任务特定指令的字符级和词级编辑，这些扰动会大幅降低下游性能。实验评估了自去噪、表示对齐等多种技术，涵盖Llama 3与Flan-T5模型，CoLA、QNLI、SST-2数据集，以及任务导向与角色导向指令。结果表明，无论使用冻结模型或微调模型，自去噪在平均性能上均显著优于其他策略，包括更复杂的集成与监督方法。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are highly vulnerable to input perturbations, as even a small prompt change may result in a substantially different output. Existing methods to enhance LLM robustness are primarily focused on perturbed data samples, whereas improving resiliency to perturbations of task-level instructions has remained relatively underexplored. In this work, we focus on character- and word-level edits of task-specific instructions, which substantially degrade downstream performance. We experiment with a variety of techniques to enhance the robustness of LLMs, including self-denoising and representation alignment, testing different models (Llama 3 and Flan-T5), datasets (CoLA, QNLI, SST-2) and instructions (both task-oriented and role-oriented). We find that, on average, self-denoising -- whether performed by a frozen LLM or a fine-tuned model -- achieves substantially higher performance gains than alternative strategies, including more complex baselines such as ensembling and supervised methods.
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