Similarities and differences between how humans and computers process language don't lie in the architecture of the processing system but in the language system itself.
Humans acquire language with very limited training data, whereas ChatGPT requires massive amounts data.ChatGPT is argued to be trained on unstructured data, but is that really so, given the way humans are trained?
The human model is trained with about 1.5 megabytes of data from their birth to adulthood to learn their native language . With relatively little training data, the human model is able to capture sentence structures, bootstrap the meaning of words, and generate words and language it has never seen or heard before. The human model does not really need to be told what is correct language use and what is not; it is able to learn this automatically.
And yet the computational and human language processing models also show remarkable differences. The amount of language data the computational model needs to be trained is very different than the amount of training data the human model needs. The computational power needed for the computational model is also very different from the power the human model needs. How can we explain these similarities and differences?models like ChatGPT are trained on unstructured data.
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