The recent developments in the field of Large Language Models (LLMs) and their applications have shown a significant shift towards enhancing the models' ability to handle structured data and improve their performance on specific tasks. A notable trend is the integration of Graph Neural Networks (GNNs) with LLMs to better process and understand structured knowledge, addressing limitations such as the inability to uniformly process various forms of structured data and the lack of adaptability to different LLMs. This approach has been particularly effective in tasks requiring structured knowledge grounding (SKG). Additionally, there is a growing focus on automating prompt optimization for LLMs using reinforcement learning, which has shown promising results in improving the performance of LLMs across various NLP tasks. Another emerging area is the use of graph databases to enhance information retrieval in fields like Material Science, where traditional methods often fall short due to outdated information and context constraints. Furthermore, advancements in generating synthetic negatives for knowledge graph embedding models have demonstrated substantial improvements in model performance, particularly in larger datasets. Lastly, the role of LLMs in knowledge graph construction is being redefined with the introduction of frameworks that leverage LLMs not just as predictors but also as judges, significantly improving the quality of constructed knowledge graphs. These developments collectively indicate a move towards more sophisticated and integrated approaches that leverage the strengths of both LLMs and graph-based methodologies to tackle complex, real-world problems.