The field of large language models is moving towards increased explainability and security, with a focus on developing systems that can provide transparent and interpretable results. This is being achieved through the use of knowledge graphs, retrieval-augmented generation, and other techniques that enable models to generate safe and accurate outputs. Noteworthy papers in this area include: Large Language Models for Explainable Threat Intelligence, which proposes a system that uses a large language model with retrieval-augmented generation to obtain threat intelligence and generate explainable results. KG-DF: A Black-box Defense Framework against Jailbreak Attacks Based on Knowledge Graphs, which introduces a knowledge graph defense framework that enhances defense performance against jailbreak attacks while improving response quality in general QA scenarios.