Paper short abstract:

This paper provides an overview of the fundamental relevance of the field of linguistic anthropology to the understanding and/or critique of Large Language Models, clarifying aspects of current debates about LLMs within NLP research as well as among those in the social science and humanities.

Paper long abstract:

At the core of the controversiality of Large Language Models (LLMs), on the one hand, their implicit rejection of influential theories in mainstream linguistics and cognitive science; and on the other, the unconscious adoption of interactional paradigms — such as the overt dialogicality of the "instruction-tuned" ChatGPT — championed more frequently in the humanities and social sciences as fundamental to sense-making. Indeed, many computer scientists in contemporary NLP do not find it necessary to concern themselves with the wide variety of past or present theories of language and learning. However, a prominent and arguably misguided assumption has been made by members of the AI research community that applying increasing scale to these models' training data, training time, and/or architectural size is likely to lead to the achievement of superhuman intelligence; this perspective, like many individualist approaches to cognition, necessarily downplays the role of indexical embodiment, social interaction, and contextually reflexive cultural practices in already-sociotechnical human communication. I will argue that a better understanding of the field known as linguistic anthropology can help understand both current and future successes and failure modes of LLMs, as well as helping social scientists and humanists to avoid some common, but misguided, avenues of critique for LLMs. From century-old works of American anthropological linguistics to the more contemporary insights of Michael Silverstein's theories of pragmatics and metapragmatics, this resolutely empirical — but semiotically and ethnographically well-grounded — school of thought provides surprising insights into both the intriguing strengths and fundamental limits of these computational artifacts.