让大模型在乱序、删词的指令上训练，能提升抗干扰能力。
Fine-Tuning on Noisy Instructions: Effects on Generalization and Performance
- 在指令中加入删词、调序等扰动进行微调
- 部分基准测试上，模型对原始和扰动指令表现更好
- 适合希望提升模型鲁棒性的应用开发者
指令微调对提升大语言模型的任务解决能力至关重要，但现有模型对指令表述的细微变化敏感。本文探究在指令微调数据中引入扰动（如删除停用词或打乱词语顺序）是否能增强大模型对噪声指令的抵抗力。研究聚焦于使用此类扰动进行微调后，模型在MMLU、BBH、GSM8K等主流基准上的原版与扰动版本表现，并分析学习动态及行为变化。结果显示，某些情况下，基于扰动指令的微调反而提升了下游性能。这表明在指令微调中纳入扰动指令，有助于提升大模型对用户输入噪声的鲁棒性。
原文摘要 · Abstract (English)
Instruction-tuning plays a vital role in enhancing the task-solving abilities of large language models (LLMs), improving their usability in generating helpful responses on various tasks. However, previous work has demonstrated that they are sensitive to minor variations in instruction phrasing. In this paper, we explore whether introducing perturbations in instruction-tuning data can enhance LLMs' resistance against noisy instructions. We focus on how instruction-tuning with perturbations, such as removing stop words or shuffling words, affects LLMs' performance on the original and perturbed versions of widely-used benchmarks (MMLU, BBH, GSM8K). We further assess learning dynamics and potential shifts in model behavior. Surprisingly, our results suggest that instruction-tuning on perturbed instructions can, in some cases, improve downstream performance. These findings highlight the importance of including perturbed instructions in instruction-tuning, which can make LLMs more resilient to noisy user inputs.
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