The field of natural language processing is rapidly advancing, with a focus on developing innovative language models that can support and automate various tasks in scientific research and analysis. Recent developments have shown significant improvements in language models' ability to evaluate and design ablation studies, extract important tokens in e-commerce queries, and analyze text data from adult service websites. Additionally, language models are being applied to improve drug identification in overdose death surveillance and automate novelty evaluation of academic papers. Notably, fine-tuned clinical variants of language models have achieved near-perfect performance in classifying specific drug involvement from unstructured death certificate text. Overall, the field is moving towards developing more advanced and reliable language models that can effectively support and augment human capabilities in scientific research and analysis. Noteworthy papers include: AblationBench, which introduces a benchmark suite for evaluating agents on ablation planning tasks in empirical AI research. AbGen, which evaluates the capabilities of large language models in designing ablation studies for scientific research. Language Models for Adult Service Website Text Analysis, which demonstrates the use of custom transformer models for analyzing text data from adult service websites. Improving Drug Identification in Overdose Death Surveillance using Large Language Models, which achieves near-perfect performance in classifying specific drug involvement from unstructured death certificate text. Automated Novelty Evaluation of Academic Paper, which integrates human and large language model knowledge to evaluate the novelty of academic papers.