Researchers introduce HALT, a lightweight hallucination detector for large language models (LLMs) that leverages log-probabilities as a time series. HALT outperforms a fine-tuned BERT-base encoder and achieves a 60x speedup gain on the HUB benchmark, which consolidates prior datasets for hallucination detection across diverse LLM capabilities.