摘要
以大模型为代表的生成式人工智能产业是发展新质生产力的关键动力.本文采用演化博弈论模型研究大模型产学研合作的特点和挑战.大模型训练需要庞大的算力和数据资源,这使得学研方独立进行大模型学术研究面临困境,需寻求业界合作;而大模型底层架构、算法模型的创新离不开学研方基础学科研究和人才培养的支持,大模型产学研合作中各方需求和禀赋不同.本文采用协同演化博弈模型,分析资源、利益和风险分配问题,并采用数值仿真法验证结论,发现算力数据资源协同的成本、产品投入应用的回报、政府激励是影响大模型产学研合作的关键要素.在此基础上提出政府完善数据算力资源共享机制、产方注重实际应用导向、学研方提升基础研究前瞻性、监管方发力保障数据应用安全等建议.
Abstract
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.
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