HALT: Hallucination Assessment via Latent Testing
This addresses the issue of unreliable outputs in LLMs for users needing fast and accurate AI responses, though it is incremental as it builds on existing uncertainty estimation methods.
The paper tackled the problem of hallucination in large language models by proposing lightweight residual probes that read hallucination risk from intermediate hidden states, achieving strong AUROC and AURAC across four QA benchmarks and multiple LLM families with effectively zero added latency.
Hallucination in large language models (LLMs) can be understood as a failure of faithful readout: although internal representations may encode uncertainty about a query, decoding pressures still yield a fluent answer. We propose lightweight residual probes that read hallucination risk directly from intermediate hidden states of question tokens, motivated by the hypothesis that these layers retain epistemic signals that are attenuated in the final decoding stage. The probe is a small auxiliary network whose computation is orders of magnitude cheaper than token generation and can be evaluated fully in parallel with inference, enabling near-instantaneous hallucination risk estimation with effectively zero added latency in low-risk cases. We deploy the probe as an agentic critic for fast selective generation and routing, allowing LLMs to immediately answer confident queries while delegating uncertain ones to stronger verification pipelines. Across four QA benchmarks and multiple LLM families, the method achieves strong AUROC and AURAC, generalizes under dataset shift, and reveals interpretable structure in intermediate representations, positioning fast internal uncertainty readout as a principled foundation for reliable agentic AI.