PSM: Prompt Sensitivity Minimization via LLM-Guided Black-Box Optimization
Published:Nov 20, 2025 10:25
•1 min read
•ArXiv
Analysis
This article introduces a method called PSM (Prompt Sensitivity Minimization) that aims to improve the robustness of Large Language Models (LLMs) by reducing their sensitivity to variations in prompts. It leverages black-box optimization techniques guided by LLMs themselves. The research likely explores how different prompt formulations impact LLM performance and seeks to find prompts that yield consistent results.
Key Takeaways
Reference
“The article likely discusses the use of black-box optimization, which means the internal workings of the LLM are not directly accessed. Instead, the optimization process relies on evaluating the LLM's output based on different prompt inputs.”