> LLMs don’t notice things - in the human manner you assumed they do. It isn't point out gaps, its repeating text patterns. Our habit of dealing with humans is

> LLMs don’t notice things - in the human manner you assumed they do. It isn't point out gaps, its repeating text patterns. Our habit of dealing with humans is filling in these gaps and supporting an assumption of ‘noticing’ or ‘improvement’. True. It is important to avoid anthropomorphizing LLMs, etc. I do this intentionally now and then but I agree it is dangerous to do it accidentally. > I think it is hard for us to consider changes in text, without seeing the changes in meaning as well. True, but since LLMs were trained on text sequences that had meaning, much of the meaning was accidentally embodied in the resulting model. Areas where LLMs seem to reason well happen to be the areas where the training data was sufficiently generalized and the language tokens used similarly enough... such as recipes. > With LLMs you have to accept that it’s not seeing meaning, it’s just seeing correlation. True. I think it is interesting how much it often feels like knowledge. I think we also have to be careful not to overly glorify human "knowledge" as something other than producing a pattern of output signals in response to a pattern of input signals.