Large language models (LLMs) like GPT are often conceptualized as passive predictors, simulators, or even stochastic parrots. We instead conceptualize LLMs by drawing on the theory of active inference originating in cognitive science and neuroscience. We examine similarities and differences between traditional active inference systems and LLMs, leading to the conclusion that, currently, LLMs lack a tight feedback loop between acting in the world and perceiving the impacts of their actions, but otherwise fit in the active inference paradigm. We list reasons why this loop may soon be closed, and possible consequences of this including enhanced model self-awareness and the drive to minimize prediction error by changing the world.

翻译：像GPT这样的大语言模型通常被概念化为被动预测器、模拟器，甚至是随机鹦鹉。我们则借鉴认知科学和神经科学中的主动推理理论来重新定义大语言模型。通过比较传统主动推理系统与大语言模型的异同，我们得出结论：当前大语言模型虽然在行动与感知行动后果之间缺乏紧密的反馈回路，但在其他方面符合主动推理范式。我们列举了该回路可能很快被闭合的原因及其潜在影响，包括增强模型自我意识，以及通过改变世界来最小化预测误差的驱动力。