Do LLMs “think” in a similar way to humans? Or is it totally different?
Maybe it’s a good idea to listen to someone who publishes papers on this very subject, and is a professor of both philosophy and psychiatry and directs an Institute for Cognitive Science. That person is Dr. Chandra Sripada and his insights are fascinating.
Sean Carroll (interviewer, scientist and science communicator) says this interview made him lean towards the answer being “yes, they think like humans” whereas previously he favored the opposite view.
The difference between the cash register and an inference engine is the training data. Also, barring something like mechanical failure, I would trust the calculation of a cash register (that has previously been demonstrated to function with high fidelity, and barring any major incidents such as dropping it onto the floor that might make me question the relevance of those prior tests to its current performance).
But the results of a LLM “calculation” - and I mean this with total sincerity - might be something like:
“What is 1+1=”?
“Answer: 6, 7!! 🤪”
Or in ye olden times, one expected response might be “ur mother!” or some other flippant remark… exactly like a small child, imitating others might do. Monkey see, monkey do => the principles undergirding LLM technology? It does not understand what it sees, hence it does not “know” when to apply what answer, only going by the most popular (whatever the weighting scheme is - probably highest upvoted answer on Reddit?) string of words that seem to be associated with the question.