The field of natural language processing is moving towards developing more advanced and specialized tools for scientific research. Recent developments have focused on improving the ability of large language models to process and generate academic text, with applications in peer review, research paper introduction generation, and survey paper creation. While these models have shown promise, they still face challenges in terms of accuracy, coherence, and comprehensiveness. Notable papers in this area include: RadarQA, which introduces a multi-modal quality analysis method for weather radar forecasts, demonstrating the potential for large language models to improve forecast evaluation. SurveyGen-I, which presents a framework for consistent scientific survey generation with evolving plans and memory-guided writing, showing improved performance in content quality and citation coverage. These developments highlight the ongoing efforts to improve the capabilities of large language models in supporting scientific research and communication.