Large models of what? Mistaking engineering achievements for human linguistic agency

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. • Hyped claims regarding abilities of Large Language Models (LLMs) are criticised. • We analyse two false assumptions: “language completeness” and “data completeness”. • Without embodiment , participation , and precariousness LLMs cannot be agents. • These absences are illustrated with the phenomenon of “algospeak”. • We highlight crucial ethical issues arising from these errors.

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Publication Details

Journal
Language Sciences
Published
2024-08-22
DOI
https://doi.org/10.1016/j.langsci.2024.101672
Citations
15
Primary Topic
Cognitive Science and Education Research
Type
article
Field-Weighted Citation Impact
5.14

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article

Large models of what? Mistaking engineering achievements for human linguistic agency

Abeba Birhane, Marek McGann
15 citations
Language Sciences
Cognitive Science and Education Research
5.14
article

Large models of what? Mistaking engineering achievements for human linguistic agency

Abeba Birhane, Marek McGann
article en
15 citations

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. • Hyped claims regarding abilities of Large Language Models (LLMs) are criticised. • We analyse two false assumptions: “language completeness” and “data completeness”. • Without embodiment , participation , and precariousness LLMs cannot be agents. • These absences are illustrated with the phenomenon of “algospeak”. • We highlight crucial ethical issues arising from these errors.

Language SciencesVol. 106
Science Foundation Ireland (IE), Trinity College Dublin (IE), Mary Immaculate College (IE)
Science Foundation Ireland, European Regional Development Fund
Quality Education
Openalex Percentile: Top 4%
Cognitive Science and Education Research
5.14
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