arXiv:2606.12476v1 Announce Type: cross Abstract: Token-level hallucination detectors are evaluated as classifiers, by AUC over all tokens, yet a streaming monitor is judged by its reaction time: the number of tokens that pass between the onset of a hallucination and the alarm. We formulate hallucination onset detection as a quickest change detection problem. A first-order Markov model of the latent faithful/hallucinated state, validated on RAGTruth, places the task inside classical change-point theory and yields Lorden's lower bound on detection delay: about 1.3 tokens at a false-alarm rate o
The proliferation of generative AI models necessitates robust methods for detecting and mitigating hallucinations, making real-time monitoring a critical current challenge.
Reliable and rapid hallucination detection is fundamental for the safe and effective deployment of AI, particularly in high-stakes applications, thereby impacting trust and adoption.
The proposed 'quickest detection' framework, with its theoretical delay bounds and learned CUSUM statistics, offers a more rigorous and effective way to monitor and alert on AI hallucination onset compared to static classifier metrics.
- · AI safety researchers
- · Generative AI developers
- · Enterprises deploying AI
- · AI monitoring platforms
- · Untrustworthy AI applications
- · Legacy hallucination detection methods
Improved reliability and safety for AI applications leveraging large language models.
Accelerated adoption of AI in sensitive domains as concerns about hallucination are systematically addressed.
The development of automated AI oversight systems that can autonomously 'self-correct' or flag issues in real-time.
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Read at arXiv cs.CL