Researchers have developed a framework called KA2L to improve the performance of large language models (LLMs) by assessing their knowledge and focusing on areas where they need improvement. The framework uses latent space analysis to identify gaps in the model's understanding and generates targeted questions to help it learn more efficiently. Experiments with nine open-source LLMs showed that KA2L reduced annotation and computation costs by 50% while achieving better performance. --- Why it matters: This matters because large language models are widely used in applications such as chatbots, virtual assistants, and text generation tools. Improving their efficiency and accuracy can have significant impacts on industries like customer service, content creation, and education. Source: https://arxiv.org/abs/2603.17566