Conversational Agents and the Understanding of Human Language: Reflections on AI, LLMs, and Cognitive Science

arXiv cs.CL / 3/31/2026

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Key Points

The paper examines how computer natural-language processing relates to theories of human language understanding from linguistics and cognitive science.
It traces the evolution of NLP paradigms from early approaches to today’s large language models, comparing each stage to human-language-capacity theories.
It argues that despite the impressive conversational abilities of current neural-network-based chatbots, NLP progress has not substantially deepened understanding of how human minds process language.
The work positions conversational agents as a point of reflection for cognitive-science questions rather than as evidence that they fully capture human language cognition.

Abstract

In this paper, we discuss the relationship between natural language processing by computers (NLP) and the understanding of the human language capacity, as studied by linguistics and cognitive science. We outline the evolution of NLP from its beginnings until the age of large language models, and highlight for each of its main paradigms some similarities and differences with theories of the human language capacity. We conclude that the evolution of language technology has not substantially deepened our understanding of how human minds process natural language, despite the impressive language abilities attained by current chatbots using artificial neural networks.