Academic collaboration on large language model studies increases overall but varies across disciplines

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 their potential for interdisciplinary collaboration, we collect and analyze data from OpenAlex, an open-source academic database. Our dataset comprises 59,293 LLM-related papers, along with 70,945 machine learning (ML) papers and 73,110 papers from non-LLM/ML fields as control groups. We first employ Shannon Entropy to assess the diversity of collaboration. Our results reveal that many fields have exhibited a more significant increasing trend following the release of ChatGPT as compared to the control groups. In particular, Computer Science and Social Science display a consistent increase in both institution and department entropy. Other fields such as Decision Science, Psychology, and Health Professions have shown minor to significant increases. Our difference-in-difference analysis also indicates that the release of ChatGPT leads to a statistically significant increase in collaboration in several fields, such as Computer Science and Social Science. In addition, we analyze the author networks and find that Computer Science, Medicine, and other Computer Science-related departments are the most prominent. Regarding authors' institutions, our analysis reveals that entities such as Stanford University, Harvard University, and University College London are key players, either dominating centrality or playing crucial roles in connecting research networks. Overall, this study provides valuable information on the current landscape and evolving dynamics of collaboration networks in LLM research. It also suggests potential areas for fostering more diverse collaborations and highlights the need for continued research on the impact of LLMs on scientific practices.