[jeroenhd]

The fun side of ChatGPT is that if you probe it for information like this, it'll also generate complete fantasy. Without an expert to consult, the generated explanation may as well conclude the earth is flat and the sky is green.

[terminalcommand]

No, this is not accurate in my trials. I use Claude.ai daily. If you ask questions on niche topics or dive down too deep, it says that resources on the topic are limited and you should consult a book.

[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.