The field of natural language processing is witnessing significant developments in the integration of knowledge graphs with large language models (LLMs). This integration aims to enhance the factual grounding and reasoning capabilities of LLMs, enabling them to better understand and generate text. Recent research has focused on developing novel approaches to combine structured knowledge from knowledge graphs with the learning capabilities of LLMs. These approaches have shown promise in improving the performance of LLMs on various tasks, including clinical note generation, mathematical reasoning, and causal discovery. Notably, the use of reinforcement learning, graph-based methods, and hypernetworks has emerged as effective techniques for integrating knowledge graphs with LLMs. Noteworthy papers in this area include Beyond RAG, which proposes a reinforced retriever for long-form discharge instruction generation, and Topology of Reasoning, which introduces the notion of a reasoning graph to understand large reasoning models. CC-RAG is also notable for its structured multi-hop reasoning via theme-based causal graphs, while Paths to Causality presents a novel approach for knowledge-based causal discovery using informative subgraphs within knowledge graphs. Additionally, PropMEND and Reliable Reasoning Path offer innovative solutions for knowledge propagation and reasoning path refinement in LLMs. Overall, these advancements have the potential to significantly improve the capabilities of LLMs and enable more accurate and interpretable results in various applications.