[论文解读] Large Models of What? Mistaking Engineering Achievements for Human Linguistic Agency

Abeba Birhane, Marek McGann|arXiv (Cornell University)|Jul 11, 2024

Design Education and Practice被引用 9

一句话总结

本文认为 LLMs 不是像人类一样的语言代理，原因在于两个错误假设（语言完整性与数据完整性），并从行动者理论的视角强调具身性、参与性与不稳定性，利用 algospeak 来说明其中的差距。

ABSTRACT

In this paper we argue that key, often sensational and misleading, claims regarding linguistic capabilities of Large Language Models (LLMs) are based on at least two unfounded assumptions; the assumption of language completeness and the assumption of data completeness. Language completeness assumes that a distinct and complete thing such as `a natural language' exists, the essential characteristics of which can be effectively and comprehensively modelled by an LLM. The assumption of data completeness relies on the belief that a language can be quantified and wholly captured by data. Work within the enactive approach to cognitive science makes clear that, rather than a distinct and complete thing, language is a means or way of acting. Languaging is not the kind of thing that can admit of a complete or comprehensive modelling. From an enactive perspective we identify three key characteristics of enacted language; embodiment, participation, and precariousness, that are absent in LLMs, and likely incompatible in principle with current architectures. We argue that these absences imply that LLMs are not now and cannot in their present form be linguistic agents the way humans are. We illustrate the point in particular through the phenomenon of `algospeak', a recently described pattern of high stakes human language activity in heavily controlled online environments. On the basis of these points, we conclude that sensational and misleading claims about LLM agency and capabilities emerge from a deep misconception of both what human language is and what LLMs are.

研究动机与目标

• 挑战声称 LLMs 完全理解或掌握人类语言。
• 将工程化的语言观念与行动性认知科学概念进行对比。
• 突出人类语言参与的具身性、参与性和不稳定性。
• 用诸如 algospeak 这样的真实世界模式来说明 LLMs 的局限性。

提出的方法

• 对 LLM 工程与行动性语言理论进行理论比较。
• 定义并应用语言完整性和数据完整性的概念。
• 阐明语言代理的具身、参与性及不稳定性方面。
• 使用 algospeak 的概念来说明语言代理在实际中的表现。

实验结果

研究问题

• RQ1 LLMs 是否在行动性意义上体现了人类语言代理？
• RQ2 哪些关键方面（具身性、参与性、不稳定性）将 LLMs 与人类语言活动区分开？
• RQ3 关于语言和数据的假设如何影响对 LLM 能力的主张？
• RQ4 algospeak 如何揭示在线互动中的语言代理？

主要发现

• LLMs 作为基于令牌的统计模型运作，产生类似语言的输出，而不等同于完整的人类语言理解。
• 两个核心假设（语言完整性和数据完整性）支撑了关于 LLMs 的耸人听闻的主张。
• 行动性语言强调具身性、参与性和不稳定性，这些在 LLMs 中是缺失的。
• 人类语言代理涉及超越文本数据的具身、互动性和以情境为基础的参与。
• algospeak 体现了高风险、受控语言使用，挑战了对 LLMs 所宣称的语言代理性的主张。