  Interdisciplinary collaboration is crucial for addressing complex scientific
challenges. Recent advancements in large language models (LLMs) have shown
significant potential in benefiting researchers across various fields. To
explore the application of LLMs in scientific disciplines and their
implications for interdisciplinary collaboration, we collect and analyze 50,391
papers from OpenAlex, an open-source platform for scholarly metadata. We first
employ Shannon entropy to assess the diversity of collaboration in terms of
authors' institutions and departments. Our results reveal that most fields have
exhibited varying degrees of increased entropy following the release of
ChatGPT, with Computer Science displaying a consistent increase. Other fields
such as Social Science, Decision Science, Psychology, Engineering, Health
Professions, and Business, Management & Accounting have shown minor to
significant increases in entropy in 2024 compared to 2023. Statistical testing
further indicates that the entropy in Computer Science, Decision Science, and
Engineering is significantly lower than that in health-related fields like
Medicine and Biochemistry, Genetics & Molecular Biology. In addition, our
network analysis based on authors' affiliation information highlights the
prominence of Computer Science, Medicine, and other Computer Science-related
departments in LLM research. Regarding authors' institutions, our analysis
reveals that entities such as Stanford University, Harvard University,
University College London, and Google are key players, either dominating
centrality measures or playing crucial roles in connecting research networks.
Overall, this study provides valuable insights into the current landscape and
evolving dynamics of collaboration networks in LLM research.
