Same Question, Different Answers: Evaluating LLM Reliability Beyond Accuracy
LLMs achieve high accuracy on benchmarks but their answers can vary under meaning-preserving paraphrases. Studying across 4 benchmarks and 13 models, the paper finds instance-level instability with mismatch rates over 23%. Despite correct knowledge often being present, it is inconsistently retrieved; a self-paraphrasing strategy can partially recover latent knowledge and improve performance. This suggests standard accuracy metrics can mask instability, and evaluating consistency across equivalent inputs is a better measure of reliability.
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[Submitted on 18 May 2026]
Title:Same Question, Different Answers: Evaluating LLM Reliability Beyond Accuracy
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Abstract:Large language models (LLMs) often achieve strong accuracy on benchmarks, yet it remains unclear how reliably they apply this knowledge when the same question is phrased in different but equivalent ways. In this work, we study how model answers change under meaning-preserving paraphrases across factual question answering and mathematical reasoning tasks. Across four benchmarks and 13 models, we find that model outputs frequently depend on the exact wording of the prompt. While overall accuracy typically changes only modestly across paraphrases, instance-level behavior is far less stable: for many questions, models alternate between correct and incorrect answers depending on phrasing, with mismatch rates reaching more than 23%. Conditioning on questions that are answered correctly in their original form reveals even larger failures measured by answer flip rates, showing that single-prompt correctness is often a poor indicator of reliability. At the same time, we find that models often produce a correct answer for at least one paraphrase of a question, suggesting that the underlying knowledge is present but inconsistently retrieved. Building on this observation, we show that a simple self-paraphrasing strategy can partially recover this latent knowledge and improve performance at inference time. Together, these findings suggest that standard accuracy metrics can mask substantial instability, and that evaluating consistency across equivalent inputs provides a clearer picture of LLM reliability.
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2607.22554 [cs.AI]
(or arXiv:2607.22554v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.22554
arXiv-issued DOI via DataCite
Submission history
From: Kazem Faghih [view email] [v1] Mon, 18 May 2026 16:45:13 UTC (404 KB)
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