Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance
LLMs are increasingly deployed in dynamic, real-world settings, where the distribution of user prompts can shift substantially over time as new tasks, prompts, and users are introduced to a deployed model. Such natural prompt distribution shift poses a major challenge to LLM reliability, particularly for specialized models designed for narrow domains or user populations. Despite attention to out-of-distribution robustness, there is very limited exploration of measuring natural prompt distribution shift in prior work, and its impact on deployed LLMs remains poorly understood. We introduce the LLM Evaluation under Natural prompt Shift (LENS) framework: a data-centric approach for quantifyin...
| Authors | Parker Seegmiller, Sarah Masud Preum |
|---|---|
| Primary category | cs.CL |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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