发表机构
Champalimaud Research; Champalimaud Centre for Restorative Neurotechnology; New York University Tandon; Indiana University（尚帕利莫德研究院; 尚帕利莫德修复神经技术中心; 纽约大学坦登工程学院; 印第安纳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究探索LLMs在语义记忆搜索中追踪增强人类心理轨迹的能力，通过语义流畅性任务验证，发现LLMs追踪预测人类记忆轨迹的能力优于其他人类。
AI 中文摘要
大型语言模型（LLMs）展现出前所未有的自然语言生成能力及多种基于文本的问题解决能力。在许多基于语言的任务（如常规编码）中，这些人工智能模型已减少甚至消除了对人类输入的需求。但LLMs并非要取代人类的认知努力，而是可作为认知工具拓展人类能力，尤其适用于需要开放式概念探索与创造性构思的任务。不过，我们尚未明确这些模型在人机交互中如何增强人类此类生成式认知能力。本研究探讨并评估LLMs在语义记忆搜索中追踪并增强人类心理轨迹的能力。为此，我们采用语义流畅性任务（SFT），这是一种经典认知范式，要求进行生成式语义记忆检索，长期以来用于表征人类的聚合思维与发散思维。我们证明，LLMs在该任务中追踪和预测人类记忆轨迹的能力优于其他人类。
英文摘要
Large language models (LLMs) exhibit unprecedented natural language generation and many text-based problem-solving capabilities. Indeed, in many language-based tasks, for example routine coding, these artificial intelligence models have reduced, or even eliminated, the need for human input. But rather than replacing human cognitive effort, LLMs may instead serve as cognitive tools to extend human abilities, particularly when they are engaged in a task requiring open-ended conceptual exploration and creative ideation. However, we are yet to understand how these models may enhance such generative human cognitive abilities in human--AI interactions. In this study, we explore and evaluate the ability of LLMs to follow and enhance human mental trajectories during semantic memory search. To test this, we use the semantic fluency task (SFT), a classic cognitive paradigm requiring generative semantic memory retrieval that has long served to characterize convergent and divergent thinking in humans. We demonstrate that an LLM's abilities to track and predict human memory trajectories in this task exceed those of other humans.
Comments18 pages, 5 figures; includes Supplementary Information
