Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding (2023)

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.

## introduction the performance of large language models (llms) has recently reached high levels (see e.g. @xcite . llms are deep neural networks with a transformer architecture @xcite , trained to predict masked words from context, using massive text datasets. 1 during training, llms learn to represent input syntactically in hierarchical form, and they also learn semantic relations @xcite , which are useful features in summarising, questionanswering, and translating text. examples of recent llms are llama @xcite , @xcite , and palm @xcite . llms have sparked a lot of debate, inside and outside academia, around the question what their successes and failures say about linguistic capacities in ai systems, but also in humans. in the first part of this paper, we scrutinize three key points recurring in arguments by experts critical of llm capacities (e.g. @xcite @xcite @xcite @xcite @xcite , although there are also experts who are more optimistic on this matter (e.g. @xcite @xcite @xcite @xcite . these points are: in sum, our aim is to contribute to a more realistic framework for understanding llms within academia and beyond, which is better grounded in empirical and philosophical work. since llms' impact on research and society likely increases in the future, properly understanding them is key. still, although we defuse various critiques of llms in this paper, it is not our purpose to advocate their deployment without ongoing reflection on their implications. examples include their environmental impact @xcite , biases @xcite , problems for educators @xcite , and ethical issues in adding human feedback @xcite , but these issues go beyond the scope of this paper. ## key points in debates on llms in this section, we qualify three key points from the debate regarding llm capacities by drawing on theoretical and empirical work. @xcite claims that whenever we prompt a llm, for example with 'the capital city of the netherlands is ...', we actually ask 'given the statistical patterns you learned from dataset y during training, what word is most likely to follow the provided sequence?', where the answer is likely 'amsterdam'. according to this assumption, this is all there is to it; we should not speak about the model's potential topographical knowledge, nor should we say that the model understands the question in any way comparable to how humans understand it. we can see similar claims in @xcite ; marcus (2022); chomsky (2023); @xcite , and in weaker form in @xcite . ## a pragmatic philosophy of llms this section sketches a more general, pragmatist philosophical context for llms. although llms are prominent in academia and society, philosophical reflection is lagging behind. this is lamentable, given that llms and the way they are deployed raise pressing philosophical questions. here we develop a pragmatist view on llms with the following claims that we will motivated with reference to philosophical pragmatism: ## discussion although in section 3 we discuss llm capacities and mental states mostly at a fundamental level, our arguments are also relevant for engineers working on concrete systems that employ llms. such systems will always require reflection on understanding and mental states in humans and machines, which our pragmatic outlook can inform. our arguments are agnostic about the explicit taxonomies and frameworks of the mental, which engineers may develop and employ in such systems, as it is the system's behaviour that the pragmatist is typically most interested in, and it can be realised in various ways. in the design of llms, no such taxonomies or frameworks exist @xcite , but it is possible that systems that do have them manifest equally complex behaviour. in a similar vein, we can imagine training scenarios that include visual (or other multi-modal) input as a proxy for grounding denotations of words in the world, which would also make llm behaviour more sophisticated, as disambiguating input is arguably simpler with an additional information channel. evidence is emerging that enriching llms with vision modules as surrogate grounding allows such models to learn new words more efficiently @xcite . a related point is that, although a pragmatic account of mental states in humans abstracts away from their complex underlying biophysical correlates, pragmatism does not entail that there is no point in trying to disclose such correlates scientifically, with the aim of opening the black box. a biophysical account of mental states may have pragmatic values for a community of scientists (see section 3.5), and also broader pragmatic value for society in that it can help us to, for example, treat dysfunctional mental states better. this biophysical account resides at a different level of explanation, and does not necessarily conflict with pragmatic accounts of the mental in general. in the case of llms, the pragmatist has similarly no principal issues with trying to find out what patterns of (artificial) neuronal activation are correlated with mental state content in llm input and output. ## conclusion the goal of this paper was to provide further reflection on llms in two ways. first, we scrutinised three key points surfacing in recurring critiques on llms, and found that on empirical and theoretical grounds, these points need more nuance. our conclusions are that llms are more than exploiters of statistical patterns; that we need better measures for evaluating thought competence in llms before we can draw conclusions; and that llms have a role to play in language acquisition, as our current best distributional models. second, we provided a philosophical context for llms from a pragmatist perspective. an unresolved question underlying various critiques of llms, is whether they have something like 'real' language understanding and intentionality. we argued that whether we attribute unobservable mental states to other entities, including the set that would constitute 'real' language understanding and intentionality, depends on how much pragmatic value this has to us, not on whether mental states are actual properties of the entities at issue. llms (in whatever future form) will become more prominent in the years to come. we hope to have contributed to a better understanding of what llms can(not) do, as well as to a philosophically informed understanding of our interaction with llms that is more than a story of mere anthropomorphisation.