arXiv:2605.28264v1 Announce Type: new Abstract: Large Language Models (LLMs) often generate factually incorrect outputs, commonly termed hallucinations, that undermine trust and limit deployment in high-stakes settings. Existing hallucination detection methods typically require multiple forward passes, or access to model internals. In this work, we provide theoretical background and empirical evidence that the distribution of token-level entropies, beyond the mean captured by perplexity or length-normalised entropy, serves as a fingerprint of hallucination, with distributional shape and tail b
The proliferation of LLMs in critical applications necessitates robust methods for detecting and mitigating their inherent tendency to hallucinate, making this research timely.
Improved hallucination detection moves LLMs closer to deployment in high-stakes environments, increasing trust and accelerating their integration into sensitive workflows.
The proposed method offers a more efficient, less intrusive way to identify LLM hallucinations, potentially streamlining model evaluation and deployment processes.
- · AI developers
- · Enterprises adopting LLMs
- · AI safety researchers
- · Current complex hallucination detection methods
- · LLM applications in high-stakes fields with unaddressed hallucination risks
More reliable LLM-powered applications emerge across various industries.
Reduced need for extensive human oversight in certain LLM outputs, lowering operational costs.
Increased public and institutional trust in AI, accelerating general AI adoption and integration into decision-making systems.
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Read at arXiv cs.AI