The Quo Vadis of the Relationship between Language and Large Language Models
This is an incremental critique for researchers in AI and linguistics, highlighting theoretical and empirical risks in using LLMs as models of language.
The paper argues that current Large Language Models (LLMs) are inadequate as scientific models of language because they lack transparency and fail to provide explanations for linguistic phenomena, concluding that they offer minimal insights into language at their current stage.
In the field of Artificial (General) Intelligence (AI), the several recent advancements in Natural language processing (NLP) activities relying on Large Language Models (LLMs) have come to encourage the adoption of LLMs as scientific models of language. While the terminology employed for the characterization of LLMs favors their embracing as such, it is not clear that they are in a place to offer insights into the target system they seek to represent. After identifying the most important theoretical and empirical risks brought about by the adoption of scientific models that lack transparency, we discuss LLMs relating them to every scientific model's fundamental components: the object, the medium, the meaning and the user. We conclude that, at their current stage of development, LLMs hardly offer any explanations for language, and then we provide an outlook for more informative future research directions on this topic.