Robustness-aware Automatic Prompt Optimization

Abstract: The performance of LLMs depends on the quality of prompts and the semantic and structural integrity of the input data. However, existing prompt generation methods primarily focus on well-structured input data, often neglecting the impact of perturbed inputs on prompt effectiveness. To address this limitation, we propose BATprompt (By Adversarial Training prompt), a novel method for prompt generation designed to withstand input perturbations (such as typos in the input). Inspired by adversarial training techniques, BATprompt demonstrates strong performance on a variety of perturbed tasks through a two-step process: adversarial perturbation and iterative optimization on unperturbed input via LLM. Unlike conventional adversarial attack methods, BATprompt does not need access to model parameters and gradients. Instead, BATprompt leverages the advanced reasoning, language understanding and self reflection capabilities of LLMs to simulate gradients, guiding the generation of adversarial perturbations and optimizing prompt performance. We evaluate BATprompt on multiple datasets across both language understanding and generation tasks. The results indicate that BATprompt outperforms existing prompt generation methods, delivering superior robustness and performance under diverse perturbation scenarios.