The field of natural language processing is moving towards more specialized and domain-specific applications, with a focus on integrating expert knowledge and context into large language models (LLMs). Recent research has shown that LLMs can be improved by incorporating domain-specific information and structured context, leading to more accurate and reliable results in high-stakes settings such as emergency medical services and climate change analysis. The use of multi-agent frameworks and retrieval-augmented generation pipelines has also been shown to be effective in improving the performance of LLMs in tasks such as event extraction and incident response. Furthermore, the development of benchmarks and evaluation frameworks for specialized knowledge processing tasks, such as ESG question answering, is facilitating the creation of more transparent and accountable AI systems. Noteworthy papers in this area include: CLINB, a climate intelligence benchmark that assesses models on open-ended, grounded, multimodal question answering tasks, demonstrating a critical dichotomy between knowledge synthesis and verifiable attribution. Knots, a large-scale multi-agent enhanced expert-annotated dataset for NOTAM semantic parsing, achieving substantial improvements in aviation text understanding and processing. MoRA-RAG, a knowledge-grounded LLM framework that transforms reconnaissance reports into a structured foundation for multi-hazard reasoning, outperforming zero-shot LLMs and state-of-the-art RAG systems.