This paper investigates the ontological characterization of Large Language Models (LLMs) like ChatGPT. Between inflationary and deflationary accounts, we pay special attention to their status as agents. This requires explaining in detail the architecture, processing, and training procedures that enable LLMs to display their capacities, and the extensions used to turn LLMs into agent-like systems. After a systematic analysis we conclude that a LLM fails to meet necessary and sufficient conditions for autonomous agency in the light of embodied theories of mind: the individuality condition (it is not the product of its own activity, it is not even directly affected by it), the normativity condition (it does not generate its own norms or goals), and, partially the interactional asymmetry condition (it is not the origin and sustained source of its interaction with the environment). If not agents, then ... what are LLMs? We argue that ChatGPT should be characterized as an interlocutor or linguistic automaton, a library-that-talks, devoid of (autonomous) agency, but capable to engage performatively on non-purposeful yet purpose-structured and purpose-bounded tasks. When interacting with humans, a "ghostly" component of the human-machine interaction makes it possible to enact genuine conversational experiences with LLMs. Despite their lack of sensorimotor and biological embodiment, LLMs textual embodiment (the training corpus) and resource-hungry computational embodiment, significantly transform existing forms of human agency. Beyond assisted and extended agency, the LLM-human coupling can produce midtended forms of agency, closer to the production of intentional agency than to the extended instrumentality of any previous technologies.

翻译：本文探讨了以ChatGPT为代表的大型语言模型（LLMs）的本体论特征。在膨胀论与紧缩论之间，我们特别关注其作为能动主体的地位。这需要详细阐释使LLMs展现其能力的架构、处理与训练流程，以及将LLMs转化为类主体系统的扩展机制。通过系统分析，我们得出结论：基于具身认知理论，LLMs未能满足自主能动性的必要与充分条件——个体性条件（其并非自身活动的产物，甚至不受自身活动的直接影响）、规范性条件（其不生成自身的规范或目标），以及部分交互不对称性条件（其并非与环境交互的起源与持续来源）。若非能动主体，那么……LLMs究竟是什么？我们认为ChatGPT应被界定为对话者或语言自动机，即"会说话的图书馆"，它缺乏（自主）能动性，却能在非目的性但具有目的结构与目的边界的任务中进行表演性参与。在人机交互过程中，一种"幽灵般"的交互成分使得与LLMs展开真实的对话体验成为可能。尽管LLMs缺乏感觉运动与生物层面的具身性，但其文本具身性（训练语料库）与资源密集型的计算具身性，正在深刻变革现有人类能动性的形态。超越辅助型与扩展型能动性，LLM与人类的耦合可能催生"迷向型能动性"，这种形态更接近意向性能动性的生成，而非以往任何技术所具有的扩展工具性。