Researchers have developed a new method to detect hallucinations in large language models (LLMs). Hallucinations occur when LLMs generate factually incorrect statements or fabricate knowledge. The new approach, called diversion decoding, actively challenges model-generated responses and extracts features that capture the LLM's uncertainty. This method outperforms existing methods with lower computational complexity, making it a more efficient solution for evaluating hallucination detection. --- Why it matters: This matters to AI engineers because it provides a more accurate way to evaluate the reliability of large language models, which are increasingly used in applications such as chatbots and virtual assistants. Source: https://arxiv.org/abs/2607.10476