Large language models (LLMs) are impressive, but they can sometimes generate incorrect or nonsensical information, a phenomenon known as 'hallucination.' Researchers are tackling this problem by developing methods to detect when an LLM is hallucinating. A recent paper introduces a fast and memory-efficient technique for training ensembles of LLMs to identify these hallucinations. The approach leverages a clever combination of pre-trained weights, low-rank adaptation (LoRA) matrices, and rank-one modifications to train multiple model instances simultaneously. By merging the LoRA matrices with the pre-trained weights after training, the method reduces the computational overhead typically associated with large ensembles. These model instances then individually weigh in on a predicted token. This creates 'uncertainty scores' that reflect disagreement among the model instances. Like a jury deliberating on a case, greater disagreement suggests lower confidence in the LLM's output. This 'uncertainty' then gets fed into a separate classifier trained to distinguish between hallucinations and accurate statements. Tests on benchmarks like SQuAD and MMLU datasets show promising results, exceeding 97% accuracy in detecting faithfulness hallucinations (when the LLM strays from given instructions). The method is also efficient, requiring only a single GPU for training and testing. However, the research also reveals ongoing challenges, particularly in detecting factual errors and handling out-of-distribution data. While this research shows significant progress in enhancing LLM reliability, future work will focus on refined uncertainty metrics and comprehensive benchmarks to further reduce the risk of AI-generated misinformation.