Enhancing LLM Robustness to Perturbed Instructions: An Empirical Study

• URL: http://arxiv.org/abs/2504.02733v1
• Date: Thu, 03 Apr 2025 16:17:56 GMT
• Title: Enhancing LLM Robustness to Perturbed Instructions: An Empirical Study
• Authors: Aryan Agrawal, Lisa Alazraki, Shahin Honarvar, Marek Rei,
• Abstract summary: We study character- and word-level edits of task-specific instructions, which substantially degrade downstream performance.<n>We find that, on average, self-denoising achieves substantially higher performance gains than alternative strategies.
• Score: 8.827173113748701
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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