LLMs do have some internal representations that predict pretty well when they are making stuff up. https://arxiv.org/abs/2509.03531v1 https://arxiv.org/abs/250

LLMs do have some internal representations that predict pretty well when they are making stuff up. https://arxiv.org/abs/2509.03531v1 https://arxiv.org/abs/2509.03531v1 - We present a cheap, scalable method for real-time identification of hallucinated tokens in long-form generations, and scale it effectively to 70B parameter models. Our approach targets \emph{entity-level hallucinations} -- e.g., fabricated names, dates, citations -- rather than claim-level, thereby naturally mapping to token-level labels and enabling streaming detection. We develop an annotation methodology that leverages web search to annotate model responses with grounded labels indicating which tokens correspond to fabricated entities. This dataset enables us to train effective hallucination classifiers with simple and efficient methods such as linear probes. Evaluating across four model families, our classifiers consistently outperform baselines on long-form responses, including more expensive methods such as semantic entropy (e.g., AUC 0.90 vs 0.71 for Llama-3.3-70B)