arXiv:2606.01033v1 Announce Type: new Abstract: When a language model hallucinates, the final answer is wrong, but the mistake is not necessarily invisible inside the model. Different internal pathways may remain uncertain, disagree in how quickly they sharpen, or commit to competing continuations before the output is produced. We introduce TriLens, a white-box detector that turns this intuition into a compact representation: at every layer, it reads the multi-head self-attention output, the feed-forward output, and the residual stream through the model's own logit lens, then records only the
The increasing prevalence and complexity of large language models necessitate more sophisticated methods for understanding and mitigating their failure modes, particularly hallucination, as AI systems are deployed in more critical applications.
Improving the interpretability and reliability of AI models directly impacts their trustworthiness and expands their deployability across various sectors, addressing a core limitation of current generation AI.
The introduction of white-box detection methods like TriLens shifts hallucination detection from post-hoc analysis to real-time, internal monitoring, potentially enabling proactive mitigation and more robust AI agents.
- · AI developers
- · AI safety researchers
- · Enterprises deploying LLMs
- · AI audit and validation firms
- · Black-box AI solutions
- · Companies reliant on unmitigated LLM outputs
- · Traditional debugging methods for AI
TriLens enables more reliable identification of hallucination within LLMs during inference, potentially reducing costly errors.
Increased trust in LLM outputs could accelerate their adoption in high-stakes domains like finance, healthcare, and law.
The development of sophisticated internal monitoring tools may lead to new architectural paradigms for AI that are inherently more transparent and controllable, fostering further research into explainable AI.
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Read at arXiv cs.AI