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This paper investigates the phenomenon of prompt sensitivity in large language models (LLMs) by introducing a novel Interaction-based Prompt Sensitivity (IPS) metric that quantifies changes in interactions resulting from subtle prompt modifications. By applying this metric to 50 open-source LLMs, the authors identify four key factors鈥攕upervised fine-tuning, increased model scales, dense architectures, and few-shot learning鈥攖hat mitigate prompt sensitivity, revealing a common mechanism where these factors reduce sensitivity in low-order interactions. The findings underscore the importance of understanding the internal dynamics of LLMs to enhance their stability and reliability in practical applications.
Subtle prompt changes can destabilize LLMs significantly, but four key factors can mitigate this sensitivity by targeting low-order interactions.
The remarkable capabilities of large language models (LLMs) are often undermined by their instability. Even subtle and semantically irrelevant changes in prompts can cause dramatic fluctuations in performance, a phenomenon known as prompt sensitivity. Previous studies typically evaluate prompt sensitivity by comparing the LLM's final outputs when prompts change. However, such coarse-grained metrics fail to explain the internal reasons for prompt sensitivity. In this paper, we introduce interactions as a fine-grained tool to analyze prompt sensitivity of LLMs. Specifically, we decompose the output score of the LLM into a set of interactions. Each interaction represents a nonlinear relationship involving a set of input variables. We discover that subtle changes to prompts can trigger severe instability in interactions, even when the outputs of the LLM remain the same. To this end, we propose an Interaction-based Prompt Sensitivity (IPS) metric by quantifying changes in interactions when we introduce subtle changes to prompts. We apply the IPS metric to 50 open-source LLMs and uncover four factors that reduce the prompt sensitivity of LLMs, including supervised fine-tuning, increased model scales, dense architectures, and few-shot learning. More crucially, we discover a common mechanism by which these four factors reduce prompt sensitivity: all four factors tend to reduce the prompt sensitivity of low-order interactions (i.e., interactions involving few input variables).
