The field of medical informatics and natural language processing is witnessing significant advancements in data harmonization and large language models. Researchers are developing innovative approaches to address the challenges of inconsistent units in large-scale clinical datasets, enhancing the scalability and accuracy of unit harmonization systems. Furthermore, there is a growing focus on improving the performance of large language models in specific domains, such as program synthesis and mathematical reasoning, through the development of high-quality pre-training datasets. Additionally, the application of large language models in clinical decision support systems and pharmacovigilance is gaining traction, with new frameworks and techniques being proposed to automate the linking of clinical data elements to controlled vocabularies and summarize adverse drug events. Noteworthy papers in this area include: Scalable Unit Harmonization in Medical Informatics, which proposes a hybrid architecture combining BM25 and sentence embeddings with a transformer-based reranker to achieve state-of-the-art performance in unit harmonization. Rewriting Pre-Training Data Boosts LLM Performance in Math and Code, which introduces two openly licensed datasets that significantly enhance LLM performance in program synthesis and mathematical reasoning. GASCADE, which presents a novel pipeline for grouped summarization of adverse drug events and demonstrates superior performance across various metrics. CDE-Mapper, which leverages Retrieval-Augmented Generation and Large Language Models to automate the linking of clinical data elements to controlled vocabularies and achieves an average of 7.2% higher accuracy improvement compared to baseline methods. Ultra-FineWeb, which proposes an efficient data filtering pipeline that improves filtering efficiency, classifier quality, and robustness, and significantly reduces experimental and inference costs.