People acquire concepts through rich physical and social experiences and use them to understand the world. In contrast, large language models (LLMs), trained exclusively through next-token prediction over language data, exhibit remarkably human-like behaviors. Are these models developing concepts akin to humans, and if so, how are such concepts represented and organized? To address these questions, we reframed the classic reverse dictionary task to simulate human concept inference in context and investigated the emergence of human-like conceptual representations within LLMs. Our results demonstrate that LLMs can flexibly derive concepts from linguistic descriptions in relation to contextual cues about other concepts. The derived representations converged towards a shared, context-independent structure that effectively predicted human behavior across key psychological phenomena, including computation of similarities, categories and semantic scales. Moreover, these representations aligned well with neural activity patterns in the human brain, even in response to visual rather than linguistic stimuli, providing evidence for biological plausibility. These findings establish that structured, human-like conceptual representations can naturally emerge from language prediction without real-world grounding. More broadly, our work positions LLMs as promising computational tools for understanding complex human cognition and paves the way for better alignment between artificial and human intelligence.

翻译：人类通过丰富的物理与社会经验获取概念，并运用这些概念理解世界。相比之下，仅通过语言数据的下一词预测训练的大型语言模型（LLMs）却展现出惊人的人类级行为。这些模型是否正在形成类人的概念？若是，这些概念如何被表征和组织？为探究这些问题，我们将经典的反向词典任务重构为模拟人类在语境中的概念推断过程，并研究了LLMs中人类级概念表征的涌现机制。实验结果表明，LLMs能够灵活地从语言描述中推导概念，并关联其他概念的语境线索。推导出的表征会收敛为一种共享的、语境无关的结构，该结构能有效预测人类在关键心理现象中的行为模式，包括相似性计算、范畴化及语义尺度评估。此外，这些表征与人类大脑的神经活动模式高度吻合——即使是对视觉刺激而非语言刺激的反应亦如此，这为其生物合理性提供了证据。这些发现证实：结构化的人类级概念表征无需现实世界 grounding 即可从语言预测中自然涌现。更广泛而言，我们的研究将LLMs定位为理解复杂人类认知的有前景的计算工具，并为提升人工智能与人类智能的对齐程度开辟了新路径。