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

TL;DR

This paper critically examines common critiques of Large Language Models, emphasizing the need for nuanced understanding and proposing a pragmatic perspective on their capacity for understanding and intentionality.

Contribution

It challenges simplistic critiques of LLMs and offers a nuanced, pragmatic framework for interpreting their capabilities and the attribution of mental states.

Findings

Critiques about LLMs parroting data need nuance

Formal vs. functional language competence distinction clarified

Understanding in LLMs can be pragmatically attributed under certain conditions

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.