To understand language models, we must separate "language" from "thought" - TechTalks

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. The conversation around large language models (LLM) is becoming more polarized with the release of advanced models such as ChatGPT. To clear out the confusion, we need a different framework to think about LLMs, argue researchers at the University of Texas at Austin and Massachusetts Institute of Technology (MIT). In a paper titled "Dissociating language and thought in large language models: a cognitive perspective," the researchers argue that to understand the power and limits of LLMs, we must separate "formal" from "functional" linguistic competence. LLMs have made impressive advances on the former, but still have a lot of work to do on the latter, the researchers say.