The integration of advanced machine learning techniques, particularly Large Language Models (LLMs), is driving significant advancements across various research areas. A notable trend is the application of LLMs to enhance model robustness and generalizability across diverse datasets and tasks. This is evident in studies evaluating LLMs on crisis-related microblogs and visual computing tasks, as well as in the development of specialized models for specific linguistic challenges like Chinese Named Entity Recognition (NER) and Chinese Spelling Check (CSC). Additionally, there is a growing interest in integrating contextualized prompts and multi-task learning frameworks to advance event extraction from literary content and visual tasks. The field also sees a rise in the creation and utilization of specialized datasets for tasks like conflict event classification and citizen report categorization, emphasizing the importance of domain-specific data in model training and evaluation. Furthermore, the integration of Graph Neural Networks (GNNs) with LLMs is enhancing graph data preprocessing and feature extraction, enabling more effective cross-graph feature alignment and node classification. These innovations are particularly impactful in scenarios where textual data is scarce or non-existent, as they allow for the synthesis of text-attributed graphs from traditional graphs. Overall, the research direction is moving towards more sophisticated, adaptable, and multimodal solutions that leverage the power of LLMs and graph-based techniques to advance various aspects of text analysis and modeling.