Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs
Authors: name: Andong Hua, name: Kenan Tang, name: Chenhe Gu, name: Jindong Gu, name: Eric Wong, name: Yao Qin
Organization: University of California, Santa Barbara

Prompt sensitivity, referring to the phenomenon where paraphrasing (i.e.,
repeating something written or spoken using different words) leads to
significant changes in large language model (LLM) performance, has been widely
accepted as a core limitation of LLMs. In this work, we revisit this issue and
ask: Is the widely reported high prompt sensitivity truly an inherent weakness
of LLMs, or is it largely an artifact of evaluation processes? To answer this
question, we systematically evaluate 7 LLMs (e.g., GPT and Gemini family)
across 6 benchmarks, including both multiple-choice and open-ended tasks on 12
diverse prompt templates. We find that much of the prompt sensitivity stems
from heuristic evaluation methods, including log-likelihood scoring and rigid
answer matching, which often overlook semantically correct responses expressed
through alternative phrasings, such as synonyms or paraphrases. When we adopt
LLM-as-a-Judge evaluations, we observe a substantial reduction in performance
variance and a consistently higher correlation in model rankings across
prompts. Our findings suggest that modern LLMs are more robust to prompt
templates than previously believed, and that prompt sensitivity may be more an
artifact of evaluation than a flaw in the models.