Large Models of What? Mistaking Engineering Achievements for Human Linguistic Agency
Abeba Birhane, Marek McGann
TL;DR
The paper interrogates sensational claims about LLM linguistic agency by contrasting engineering and enactive theories of language. It argues that language should be understood as enacted, embodied practice characterized by embodiment, participation, and precarity, which current LLMs cannot realize. Using algospeak and procedurally generated game analogies, it shows LLM outputs are grounded in token statistics and interface constraints rather than mutual meaning-making. The work highlights implications for evaluation, policy, and social justice, urging rigorous scrutiny before deploying such technologies.
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.