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November 8, 2025Open Access

Hallucination Detection on a Budget: Efficient Bayesian Estimation of Semantic Entropy

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Authors

KCKamil CiosekNFNicolò FelicioniSGSina Ghiassian

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Overview

Algorithm improves hallucination detection quality in LLMs by using fewer samples, suggesting effective adaptation strategies.

Key Points

  • Achieving improved detection quality involves using only 53% of the samples compared to previous methods.
  • Evaluation of LLM performance showed a successful algorithm based on Bayesian estimation of quality metrics.
  • The developed algorithm adapts sample sizes for challenging contexts, enhancing overall detection effectiveness.
  • Insights from this study support more efficient approaches to identify hallucinations in language models.

Cite This Study

Ciosek et al. (2025) studied this question.

synapsesocial.com/papers/690e8b75a5b062d7a4e737cfhttps://doi.org/10.48550/arxiv.2504.03579
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