Ideas generated by independent samples of humans tend to be more diverse than ideas generated from independent LLM samples, raising concerns that widespread reliance on LLMs could homogenize ideation and undermine innovation at a societal level. Drawing on cognitive psychology, we identify (both theoretically and empirically) two mechanisms undermining LLM idea diversity. First, at the individual level, LLMs exhibit fixation just as humans do, where early outputs constrain subsequent ideation. Second, at the collective level, LLMs aggregate knowledge into a unified distribution rather than exhibiting the knowledge partitioning inherent to human populations, where each person occupies a distinct region of the knowledge space. Through four studies, we demonstrate that targeted prompting interventions can address each mechanism independently: Chain-of-Thought (CoT) prompting reduces fixation by encouraging structured reasoning (only in LLMs, not humans), while ordinary personas (versus "creative entrepreneurs" such as Steve Jobs) improve knowledge partitioning by serving as diverse sampling cues, anchoring generation in distinct regions of the semantic space. Combining both approaches produces the highest idea diversity, outperforming humans. These findings offer a theoretically grounded framework for understanding LLM idea diversity and practical strategies for human-AI collaborations that leverage AI's efficiency without compromising the diversity essential to a healthy innovation ecosystem.

翻译：人类独立样本生成的想法通常比独立大语言模型样本生成的想法更具多样性，这引发了广泛担忧：对大语言模型的普遍依赖可能导致思维同质化，并在社会层面损害创新。借鉴认知心理学，我们从理论和实证两方面识别出削弱大语言模型想法多样性的两种机制。首先，在个体层面，大语言模型与人类一样表现出思维固着现象，即早期输出会制约后续的构思过程。其次，在集体层面，大语言模型将知识聚合为单一分布，而非呈现人类群体固有的知识分区特性——每个人占据知识空间的不同区域。通过四项研究，我们证明针对性的提示干预能独立应对每种机制：思维链提示通过鼓励结构化推理（仅对大语言模型有效，对人类无效）减少思维固着；而普通人物角色（相对于史蒂夫·乔布斯等“创意企业家”）通过作为多样化采样线索，将生成过程锚定在语义空间的不同区域，从而改善知识分区。结合两种方法可产生最高的想法多样性，甚至超越人类表现。这些发现为理解大语言模型想法多样性提供了理论框架，并为人类与人工智能协作提供了实用策略，既能利用人工智能的效率，又不会损害健康创新生态系统所必需的多样性。