arXiv:2606.12160v1 Announce Type: new Abstract: In this work, we introduce CHAIR (Classifier of Hallucination As ImproveR), a supervised framework for detecting hallucinations by analyzing internal logits from each layer of every token. Our method extracts a compact set of features such as maximum, minimum, mean, standard deviation, and slope-from the token logits across all layers, enabling effective hallucination detection without overfitting. Experiments on TruthfulQA and MMLU datasets demonstrate that CHAIR significantly improves detection accuracy, particularly in zero-shot scenarios, sho
The proliferation of LLMs highlights an urgent need to address reliability and truthfulness, making novel detection methods highly relevant. Ongoing research into LLM transparency and control necessitates advanced techniques for understanding internal model states.
Improving the detection of hallucinations directly enhances the trustworthiness and utility of AI systems, which is crucial for their broader adoption in critical applications. This advancement impacts the foundational reliability of LLMs, a core component of future digital infrastructure.
The ability to more effectively detect hallucinations at decoding-time means LLMs can be deployed with greater confidence in their outputs. This shifts the focus from simply generating text to generating verifiably more truthful text.
- · AI developers
- · LLM integrators
- · Enterprise AI users
- · Data scientists
- · Unreliable LLM providers
- · Manual content verification processes
Increased user trust in AI-generated content, particularly in knowledge-intensive domains.
Reduced operational risks and costs associated with deploying AI, accelerating adoption in regulated industries.
New regulatory frameworks and industry standards for 'truthfulness' in AI, potentially tied to such validation methodologies.
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Read at arXiv cs.CL