The field of large language models is rapidly advancing, with a focus on improving evaluation methods and knowledge extraction techniques. Recent developments have highlighted the need for efficient and accurate evaluation frameworks, as well as the importance of identifying knowledge deficiencies in large language models. Researchers are exploring new approaches to discover and address errors in language models, including stochastic optimization processes and hierarchical retrieval methods. Additionally, there is a growing interest in leveraging large language models for automated definition extraction and taxonomy evaluation. These advancements have the potential to significantly improve the reliability and performance of large language models, enabling them to better retain factual knowledge and provide more accurate outputs. Noteworthy papers include Discovering Knowledge Deficiencies of Language Models on Massive Knowledge Base, which proposes a scalable framework for discovering knowledge deficiencies in closed-weight language models, and RECKON: Large-scale Reference-based Efficient Knowledge Evaluation for Large Language Model, which introduces a reference-based evaluation method that reduces resource consumption while achieving high accuracy.