arXiv:2505.20045v3 Announce Type: replace Abstract: While large language models (LLMs) have become highly capable, they remain prone to factual inaccuracies, commonly referred to as "hallucinations." Uncertainty quantification (UQ) offers a promising way to mitigate this issue, but most existing methods are computationally intensive and/or require supervision. In this work, we propose Recurrent Attention-based Uncertainty Quantification (RAUQ), an unsupervised and efficient framework for identifying hallucinations. The method leverages an observation about transformer attention behavior: when
The proliferation of LLMs makes hallucination a critical problem, driving urgent research into effective and efficient detection methods.
Improved hallucination detection enhances the reliability and trustworthiness of LLMs, accelerating their adoption in sensitive applications.
The proposed method offers an unsupervised and computationally efficient way to identify hallucinations, potentially making UQ more widely accessible.
- · LLM developers
- · AI ethicists
- · Enterprises adopting LLMs
- · Inefficient UQ methods
More reliable AI applications become feasible due to reduced hallucination risk.
Public trust and regulatory acceptance of LLMs could increase, fostering broader integration into critical infrastructure.
The competitive landscape for LLM providers shifts towards those who can demonstrate superior hallucination mitigation capabilities.
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