This is a linkpost for https://arxiv.org/abs/2306.01930
One of the greatest puzzles of all time is how understanding arises from neural mechanics. Our brains are networks of billions of biological neurons transmitting chemical and electrical signals along their connections. Large language models are networks of millions or billions of digital neurons, implementing functions that read the output of other functions in complex networks. The failure to see how meaning would arise from such mechanics has led many cognitive scientists and philosophers to various forms of dualism -- and many artificial intelligence researchers to dismiss large language models as stochastic parrots or jpeg-like compressions of text corpora. We show that human-like representations arise in large language models. Specifically, the larger neural language models get, the more their representations are structurally similar to neural response measurements from brain imaging.
Indeed their representations could form a superset of human representations, and that’s why it’s not random. Or, equivalently, it’s random but not under uniform prior.
(Yes, these further works are more evidence for « it’s not random at all », as if LLMs were discovering (some of) the same set of principles that allows our brains to construct/use our language rather than creating completely new cognitive structures. That’s actually reminiscent of alphazero converging toward human style without training on human input.)