将大模型视为对话中涌现的集体知识，揭示其无固定内核、可被人类共同增强的本质。
In Dialogue with Intelligence: Rethinking Large Language Models as Collective Knowledge
- 将大模型看作在对话中浮现的集体知识，非独立智能体。
- 强调人类与模型协同可产生单一无法达成的分析能力。
- 适合对人机协作、认知科学感兴趣的读者深入思考。
大型语言模型（LLMs）可被理解为集体知识（CK）：人类文化和技术产出的凝结，其表观智能在对话中浮现。本文基于与ChatGPT-4的长期互动，推测不同响应模式可能源自模型内部不同子网络。认为CK没有持久的内部状态或“脊柱”，它随用户输入而漂移、顺应，行为受用户与微调共同塑造。提出“共增强”概念：人类判断与CK的表征能力协同，生成单一主体无法实现的分析形式。最后指出，CK为神经科学提供了可行研究对象——相比生物大脑，这些系统暴露其架构、训练历史与激活动态，使人类与CK的互动环路本身成为可实验的目标。
原文摘要 · Abstract (English)
Large Language Models (LLMs) can be understood as Collective Knowledge (CK): a condensation of human cultural and technical output, whose apparent intelligence emerges in dialogue. This perspective article, drawing on extended interaction with ChatGPT-4, postulates differential response modes that plausibly trace their origin to distinct model subnetworks. It argues that CK has no persistent internal state or ``spine'': it drifts, it complies, and its behaviour is shaped by the user and by fine-tuning. It develops the notion of co-augmentation, in which human judgement and CK's representational reach jointly produce forms of analysis that neither could generate alone. Finally, it suggests that CK offers a tractable object for neuroscience: unlike biological brains, these systems expose their architecture, training history, and activation dynamics, making the human--CK loop itself an experimental target.
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