证明大模型无意识：缺乏持续学习是根本原因
A Disproof of Large Language Model Consciousness: The Necessity of Continual Learning for Consciousness
- 从可证伪性出发，否定大模型具备意识的理论基础
- 功能等价系统无法拥有可检验的意识理论，反推大模型无意识
- 持续学习可能是意识的关键，适合研究意识机制的学者
意识理论必须具备可证伪性和非平凡性。近期研究提供了形式化工具来检验这些要求。令人意外的是，许多当代意识理论未能通过此标准，包括基于因果结构的理论，以及本文所展示的基于功能的理论。本文指出，由于大语言模型（LLMs）与其输入输出功能等价的系统高度相似，而这类系统无法存在可证伪且非平凡的意识理论，因此可构成对当前大模型意识的反证。进一步地，本文证明基于或需要持续学习的意识理论，能满足人类意识理论的严格形式约束。这支持一个假说：若持续学习与人类意识相关，则大模型因不具备持续学习能力，其意识缺失与此密切相关。
原文摘要 · Abstract (English)
Scientific theories of consciousness should be falsifiable and non-trivial. Recent research has given us formal tools to analyze these requirements of falsifiability and non-triviality for theories of consciousness. Surprisingly, many contemporary theories of consciousness fail to pass this bar, including theories based on causal structure but also (as I demonstrate) theories based on function. Herein, I show these requirements of falsifiability and non-triviality especially constrain the potential consciousness of contemporary Large Language Models (LLMs) because of their proximity to systems that are equivalent to LLMs in terms of input/output function; yet, for these functionally equivalent systems, there cannot be any falsifiable and non-trivial theory of consciousness that judges them conscious. This forms the basis of a disproof of contemporary LLM consciousness. I then show a positive result, which is that theories of consciousness based on (or requiring) continual learning do satisfy the stringent formal constraints for a theory of consciousness in humans. Intriguingly, this work supports a hypothesis: If continual learning is linked to consciousness in humans, the current limitations of LLMs (which do not continually learn) are intimately tied to their lack of consciousness.
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