Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding

Bram Van Dijk, Tom Kouwenhoven, Marco Spruit, Max Johannes van Duijn

Submission Type: Regular Long Paper

Submission Track: Theme Track: Large Language Models and the Future of NLP

Submission Track 2: Language Modeling and Analysis of Language Models

Keywords: Large Language Models, Critical Analysis, Interpretation, Explanation, Philosophy, Understanding, Pragmatism, NLP

TL;DR: This paper critically analyzes recurring points in critiques of LLMs, and provides a philosophical pragmatic perspective on language understanding and intentionality

Abstract: Current Large Language Models (LLMs) are unparalleled in their ability to generate grammatically correct, fluent text. LLMs are appearing rapidly, and debates on LLM capacities have taken off, but reflection is lagging behind. Thus, in this position paper, we first zoom in on the debate and critically assess three points recurring in critiques of LLM capacities: i) that LLMs only parrot statistical patterns in the training data; ii) that LLMs master formal but not functional language competence; and iii) that language learning in LLMs cannot inform human language learning. Drawing on empirical and theoretical arguments, we show that these points need more nuance. Second, we outline a pragmatic perspective on the issue of `real' understanding and intentionality in LLMs. Understanding and intentionality pertain to unobservable mental states we attribute to other humans because they have pragmatic value: they allow us to abstract away from complex underlying mechanics and predict behaviour effectively. We reflect on the circumstances under which it would make sense for humans to similarly attribute mental states to LLMs, thereby outlining a pragmatic philosophical context for LLMs as an increasingly prominent technology in society.

Submission Number: 2787