AI Research
Same Question, Different Answers: Evaluating LLM Reliability Beyond Accuracy
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 par...
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ArXiv CS.AI
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ArXiv CS.AI
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LLMs can struggle with semantic consistency, often providing different answers to the same question phrased differently. This instability, masked by overall accuracy metrics, highlights the need for evaluating reliability beyond simple correctness.
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