当前您的位置:

首页 < 首页 < 学术日历 <

正文

Large Language Model Based Brainstorming (LBB) for Idea Generation and Evaluation
来源: | 发布时间:2025-06-24 | 点击:


主讲嘉宾:孙河山 教授


讲座时间:2025年6月26日(周四)上午9:30


讲座地点:重庆大学B区经济与工商管理学院106教室


嘉宾简介:

孙河山教授目前任职于美国俄克拉荷马大学普莱斯商学院信息管理系统系。他的研究聚焦于信息技术对个人、组织和社会的深刻影响和相互作用,具体包括人机交互、商业分析、在线/数字行为等。他在MIS Quarterly(5篇,其中独作发表2篇)、Information Systems Research(4篇)、Journal of the Association for Information SystemsDecision Support Systems、International Journal of Human-Computer StudiesJournal of the American Society for Information Science and Technology等信息管理领域的国际顶级学术期刊发表了多篇学术论文,在2018-2022年“Worldwide on the Top IS Researcher List”中排名第45位。他目前是Information Systems Research的Editorial Review Board Member,并担任MIS quarterly、Journal of the Association for Information Systems和AIS Transactions on HCI的SeniorEditor。

 

讲座摘要:

Brainstorming has become a widely adopted technique for idea generation within organizations. However, traditional in-person brainstorming sessions are subject to several well-documented limitations such as production blocking, social anxiety, social loafing, and bias against nonconforming ideas. Recent advancements in large language models (LLMs) offer promising avenues for reimagining and enhancing the brainstorming process. In this research, I aim to achieve new theoretical understanding of how LLM-based brainstorming (LBB) simulates human brainstorming (in conversational and nominal sessions) for idea generation. Hypotheses were developed based on the recent breakthroughs in few-shot learning and chain-of-thought prompting. To empirically test these hypotheses, I conducted a series of simulated brainstorming sessions utilizing GPT-4o agents. The output from these sessions was assessed using both an LLM-based evaluator agent and human raters. The findings largely support both hypotheses and reveal several noteworthy insights with implications for both research and practices.

请升级浏览器版本

你正在使用旧版本浏览器。请升级浏览器以获得更好的体验。