Detecting hallucinations in Large Language Model (LLM) outputs is critical for safe deployment, yet existing methods require white-box access to internals, depend on multiple generations or auxiliary judge models, or collapse token-level uncertainty into scalar summaries that discard temporal structure. We introduce HALT, a lightweight grey-box detector that treats the top-20 log-probabilities of an LLM generation as a multivariate time series and processes them with a small bidirectional GRU (5M parameters), in a single forward pass and using no model internals. We evaluate on HUB, a unified protocol reorganizing existing hallucination datasets into ten capability clusters spanning factual and logical failure modes. HALT matches or surpasses Lettuce, a fine-tuned ModernBERT-base detector, on macro-F₁ while using ∼30× fewer parameters; a controlled comparison against a text classifier trained on HALT’s own data confirms this advantage comes from the log-probability signal itself rather than from differences in training data, and the same recipe applies across eight LLM families from 360M to 70B parameters.

Modular AI systems can be developed using LLM-prompts-based modules to minimize deployment time even for complex tasks. However, these systems do not always perform well and improving them using the data traces collected from a deployment remains an open challenge. The data traces contain LLM inputs and outputs, but the annotations from LLMs are noisy. We hypothesize that Active Label Correction (ALC) can be use on the collected data to train smaller task-specific improved models that can replace LLM-based modules. In this paper, we study the noise in three GPT-3.5-annotated datasets and their denoising with human feedback. We also propose a novel method ALC3 that iteratively applies three updates to the training dataset: auto-correction, correction using human feedback and filtering. Our results show that ALC3 can lead to oracle performance with feedback on 17-24% fewer examples than the number of noisy examples in the dataset across three different NLP tasks.