[jasim]

I'm curious to hear more about this. I've seen very little hallucination with mainstream LLMs where the conversation revolves around concepts that were well-represented in the training data. Most educational topics thus have been quite solid. Even asking for novel analogies between distant and unrelated topics seem to work well.

[giraffe_lady]

I haven't messed with it in a few months but something that used to consistently cause problems was asking specific questions about hypotheticals where there may be a non-matching real example in the dataset.

Kind of hard to explain but for example giving a number of at-bats and hits for a given year for a baseball player and asking it to calculate their batting average from that. If you used a real player's name it would pull some or all of their actual stats from that year, rather than using the hypothetical numbers you provided.

I think this specific case has been fixed, and with stats-based stuff like this it's easy to identify and check for. But I think this general type of error is still around.

[jasim]

Thanks, that makes sense. I avoid using LLMs for math because it is only a text token prediction system (but a magical one at that), and can't do true numeric computation. But making it write code to compute works well.