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. Our proposed framework outperforms single-LLM approaches and existing multi-LLM frameworks across various creativity metrics.

翻译：大型语言模型（LLM）在自然语言处理中展现出卓越能力，但在面对开放式问题时，生成创造性及原创性回答的能力仍显不足。为提升LLM的创造力，我们的核心洞察在于模拟人类通过邀请不同背景与视角的参与者展开讨论，从而激发集体创造力的过程。为此，我们提出LLM讨论框架——一个三阶段讨论框架：首先促进激烈且多元的观点碰撞，最终实现向创造性答案的收敛。此外，我们采用角色扮演技术，通过为LLM分配差异化角色来缓解其同质化问题。我们通过替代用途测试、相似性测试、实例测试及科学创造力测试，并借助LLM评估与人类研究双重验证，评估了所提框架的有效性。实验表明，该框架在多项创造力指标上均优于单LLM方法及现有多LLM框架。