Research on Industry-Academia-Research Mechanism of LLMs:Based on Evolutionary Game Theory and Simulation Results
The generative AI industry,exemplified by Large Language Models(LLMs),is a crucial driver of new quality productive forces.This paper utilizes an evolutionary game theory model to explore industry-academia-research collaboration challenges within the LLM domain.Training LLMs demands extensive computational and data resources,hindering independent academic research and neces-sitating industry collaboration.However,innovation in LLM architecture and algorithms relies on fundamental scientific research and ac-ademic talent cultivation,resulting in varied needs among collaborators.Resource allocation,interests,and risk distribution issues are analyzed via cooperative evolutionary game modeling,corroborated by numerical simulations.Key factors affecting industry-academia-re-search collaboration on LLMs include the cost of cooperative resources,product application ROI,and government incen-tives.Recommendations include enhancing data and computing power sharing mechanisms,industry focus on practical applications,ac-ademic foresight improvement,and regulatory efforts for data security.
Large language modelIndustry-Academia-Research collaborationCollaborative evolutionary game