arXiv:2301.06627cs.CL
Dissociating language and thought in large language models
Abstract
Large Language Models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split. Here, we evaluate LLMs using a distinction between formal linguistic competence -- knowledge of linguistic rules and patterns -- and functional linguistic competence -- understanding and using language in the world. We ground this distinction in human neuroscience, which has shown that formal and functional competence rely on different neural mechanisms. Although LLMs are surprisingly good at formal competence, their performance on functional competence tasks remains spotty and often requires specialized fine-tuning and/or coupling with external modules. We posit that models that use language in human-like ways would need to master both of these competence types, which, in turn, could require the emergence of mechanisms specialized for formal linguistic competence, distinct from functional competence.
Here's the problem with LLMs: https://arxiv.org/abs/2301.06627 Basically, they're missing a lot of the brain machinery required to function. For example, if you ask them to solve a math problem, they do just fine ... until you ask them to apply an inference rule on top of it…
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