Researchers have proposed a new method to improve confidence estimates for large language models. The approach involves building simple classifiers that predict the correctness of model responses using existing uncertainty quantification scores and the correctness of similar queries as features. This method is said to outperform existing methods with minimal computational overhead, making it suitable for real-world applications. --- Why it matters: This matters because accurate confidence estimates are crucial for safely deploying large language models in practice. The proposed method provides a way to improve these estimates without requiring significant computational resources or labelled data. Source: https://arxiv.org/abs/2608.19323