Large language models (LLMs) have shown exceptional proficiency in natural language processing but often fall short of generating creative and original responses to open-ended questions. To enhance LLM creativity, our key insight is to emulate the human process of inducing collective creativity through engaging discussions with participants from diverse backgrounds and perspectives. To this end, we propose LLM Discussion, a three-phase discussion framework that facilitates vigorous and diverging idea exchanges and ensures convergence to creative answers. Moreover, we adopt a role-playing technique by assigning distinct roles to LLMs to combat the homogeneity of LLMs. We evaluate the efficacy of the proposed framework with the Alternative Uses Test, Similarities Test, Instances Test, and Scientific Creativity Test through both LLM evaluation and human study. The results show that our proposed framework outperforms single-LLM approaches and existing multi-LLM frameworks across various creativity metrics. The code is available at https://github.com/lawraa/LLM-Discussion.
Evaluating Large Language Model Creativity from a Literary Perspective
Imperial College London（帝国理工学院）；Google DeepMind（谷歌DeepMind）；Institute of Historical Research, School of Advanced Study, University of London（伦敦大学高级研究院历史研究所）；University of Southampton（南安普顿大学）
CreativityNeuro: Steering Language Model Weights to Improve Divergent Thinking and Reduce Mode Collapse
Center for Brain Science, Harvard University（哈佛大学脑科学中心）；CBS-NTT Program in Physics of Intelligence, Harvard University（哈佛大学CBS-NTT智能物理项目）；Prior Computers；AI Innovation Institute, Stony Brook University（石溪大学人工智能创新研究所）
A Framework for Collaborating a Large Language Model Tool in Brainstorming for Triggering Creative Thoughts
R.B. Annis School of Engineering（R.B.安尼斯工程学院）；University of Indianapolis（印第安纳波利斯大学）；School of Journalism and Strategic Communication（新闻与战略传播学院）；Ball State University（鲍尔州立大学）
CreativeBench: Benchmarking and Enhancing Machine Creativity via Self-Evolving Challenges
Southern University of Science and Technology（南方科技大学）；Tsinghua University（清华大学）；Peking University（北京大学）；University of Bristol（布里斯托大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Xi’an Jiaotong University（西安交通大学）
Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Center for Data Science, Peking University（北京大学数据科学中心）；Key Laboratory of Intelligent Press Media Technology, Peking University（北京大学智能新闻媒体技术重点实验室）
Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias
Singapore General Hospital（新加坡中央医院）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Center（新加坡国家眼科中心）；Byers Eye Institute, Stanford University（斯坦福大学拜尔斯眼科研究所）；National University of Singapore（新加坡国立大学）
