Emerging technologies like AI have considerable potential for improving our understanding of language and by extension the evolution of human cognitive capacities.3
Large language models (LLMs) now achieve high accuracy on many language‑related tasks.4 While they are built on neural networks,5 LLMs have a completely different architecture to that of the human brain.6 Nevertheless, by exploring their capabilities and comparing their behaviour with that of humans, we may gain insights into the mechanisms by which languages are learned,7 including how our minds infer the structures and rules of a language.8 If this bears fruit, the result will be improved theories of language and of the relationship between language and other high-level cognitive capacities:9 for instance, the degree to which complex reasoning depends on language.10 A key future goal of AI models is to increase their multilingual coverage, including “under-resourced” languages, where training data is often sparse.13 If this is achieved, a key goal should be to leverage LLMs to document and preserve endangered languages,14 and to revitalise those languages that are under-represented on the world stage and online.15 For instance, improved retrieval technologies could be used to create collections of readable texts, which are currently unavailable for many languages.16 Computer models of language, including LLMs, can also shed light on how and why languages change over time — an inevitable process that happens to all languages. Research in cultural evolution shows that successive generations of language users implicitly shape their language,17 for instance making it both easier to learn and more useful for communication. Building scaled-up models of communicating agents can help us answer questions about the effects of individual-, group- and population-level dynamics on this process.
