ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs
Authors: name: Jingming Zhuo, name: Songyang Zhang, name: Xinyu Fang, name: Haodong Duan, name: Dahua Lin, name: Kai Chen

Large language models (LLMs) have demonstrated impressive capabilities across
various tasks, but their performance is highly sensitive to the prompts
utilized. This variability poses challenges for accurate assessment and user
satisfaction. Current research frequently overlooks instance-level prompt
variations and their implications on subjective evaluations. To address these
shortcomings, we introduce ProSA, a framework designed to evaluate and
comprehend prompt sensitivity in LLMs. ProSA incorporates a novel sensitivity
metric, PromptSensiScore, and leverages decoding confidence to elucidate
underlying mechanisms. Our extensive study, spanning multiple tasks, uncovers
that prompt sensitivity fluctuates across datasets and models, with larger
models exhibiting enhanced robustness. We observe that few-shot examples can
alleviate this sensitivity issue, and subjective evaluations are also
susceptible to prompt sensitivities, particularly in complex,
reasoning-oriented tasks. Furthermore, our findings indicate that higher model
confidence correlates with increased prompt robustness. We believe this work
will serve as a helpful tool in studying prompt sensitivity of LLMs. The
project is released at: https://github.com/open-compass/ProSA .