In Dialogue with Intelligence: Rethinking Large Language Models as Collective Knowledge

• URL: http://arxiv.org/abs/2505.22767v3
• Date: Mon, 03 Nov 2025 12:13:58 GMT
• Title: In Dialogue with Intelligence: Rethinking Large Language Models as Collective Knowledge
• Authors: Eleni Vasilaki,
• Abstract summary: Large Language Models (LLMs) can be understood as Collective Knowledge (CK): a condensation of human cultural and technical output.<n>This article postulates differential response modes that plausibly trace their origin to distinct modelworks.
• Score: 1.624454100511275
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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