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Notes -
This feels like the same kind of overly simplistic reductionism that the behaviorists engaged in.
I think internal mental states are a sensible thing to talk about. There are chatbots we can be very sure have no internal mental states: The very simple ones (like Eliza), but also the ones that would take more space than the entire universe like Ned Block's Blockhead thought experiment of a chatbot consisting of a giant lookup table of every possible sentence of some arbitrary length.
But for entities between those two extremes, we have to learn more about how they're actually working in order to say whether they have internal mental states or not.
While it is far from definitive, I remember the interpretability research on ChessGPT (an LLM trained only on chess games in chess notation), found that there was representation of the state of the chess board inside the LLM, because it turns out that the best way to predict the next move in a game of chess is to realize that there is a chess board with pieces on it, and particular moves are legal for certain pieces. That is, you must be able to reverse engineer chess to predict the next token in chess notation.
I wonder what the implications of that are for LLMs that do a reasonable job of replicating the emotional arc of a conversation with a person? I don't actually think it is totally implausible that the best way to predict what a human will say next is to essentially reverse engineer human cognition. Maybe what an LLM is doing when it plays the part of helpful assistant is that it is actually doing something very analogous to what a helpful human assistant's brain would be doing under the same circumstances?
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