[论文解读] Transforming Agency. On the mode of existence of Large Language Models

Xabier E. Barandiaran, Lola S. Almendros|arXiv (Cornell University)|Jul 15, 2024

Artificial Intelligence in Healthcare and Education被引用 5

一句话总结

本文分析大型语言模型（LLMs）是否为自主代理，并认为它们不是；相反，LLMs 更适合被描述为对话者或语言自动体，其文本体现和计算强度改变人类能动性，可能产生介于两种能动性之间的形式。

ABSTRACT

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.

研究动机与目标

• 调查大型语言模型（LLMs）如ChatGPT的本体论特征。
• 评估LLMs是否符合具身心灵理论中的自主性条件（个体性、规范性、互动性不对称性）。
• 提出对LLMs及其在人机交互中的作用的另一种表征。
• 考察文本化体现和计算体现如何改变现有的人类能动性形式。

提出的方法

• 对LLM的体系结构、处理过程和训练程序进行系统分析。
• 审视用于使LLMs具备代理特征的扩展。
• 将LLMs与具身心灵理论进行比较以评估自主性条件。
• 主张将LLMs重新表征为对话者或语言自动体，而非自主代理。

实验结果

研究问题

• RQ1 LLMs是否满足个体性条件（由自身活动产出并直接受到其影响）？
• RQ2 LLMs是否满足规范性条件（产生自身的规范或目标）？
• RQ3 LLMs是否满足互动性不对称性条件（成为与环境交互的起点和持续源）？
• RQ4 若非代理，LLMs的适当本体地位或表征为何？
• RQ5 人类与LLM的互动在实际中如何改变人类能动性？

主要发现

• LLMs未能满足具身心灵理论中关于自主代理的充分必要条件（个体性、规范性，以及部分互动性不对称性）。
• 应将ChatGPT表征为对话者或语言自动体，而非自主代理。
• LLMs的作用如同一个会说话的图书馆，缺乏自主代理，但在有目的地结构化和有界任务上可进行表演性参与。
• 文本化体现（训练语料）和资源密集型计算体现显著改变人类能动性，超越传统的辅助性或扩展性能动性。
• 人类与LLMs的耦合可能产生介于两者之间的能动性形式，更接近有意向的能动性，而非早前技术。