| I think a counterargument would be parallel evolution: There are various examples in nature, where a certain feature evolved independently several times, without any genetic connection - from what I understand, we believe because the evolutionary pressures were similar. One obvious example would be wings, where you have several different strategies - feathers, insect wings, bat-like wings, etc - that have similar functionality and employ the same physical principles, but are "implemented" vastly differently. You have similar examples in brains, where e.g. corvids are capable of various cognitive feats that would involve the neocortex in human brains - only their brains don't have a neocortex. Instead they seem to use certain other brain regions for that, which don't have an equivalent in humans. Nevertheless it's possible to communicate with corvids. So this makes me wonder if a different "implementation" always necessarily means the results are incomparable. In the interest of falsifiability, what behavior or internal structures in LLMs would be enough to be convincing that they are "real" emotions? |
However, I believe that designing such a system would be pointless and very wrong.
While emotions are something that is normally associated with a physical system that encounters in the real world various helpful or harmful experiences, one could make a program that simulates completely such a physical systems with emotions; as it lives in a simulated world, and then one could say that this program has emotions.
On the other hand, unlike with the kind of program that I have mentioned before, I do not agree that an LLM has emotions, but only that it mimics human emotions, as they had been recorded in the training texts.
There is no component of an LLM that can intrinsically generate emotions. An LLM that is trained only on texts without emotions, e.g. on program sources stripped of comments, will not show any emotion whatsoever, regardless of what you put in its input prompt.
On the other hand, when you train an LLM on texts that record human emotions, then whenever the LLM input contains something that is similar to what has elicited the human emotions recorded in the training texts, then the LLM will output a token probability distribution that will generate a response similar to the reactions of humans. Unlike a book or an audio or video recording, the output of the LLM usually will not match exactly one human emotion recording, but it will mix many of those recorded, but it will still be limited by the content used for training.