this post was submitted on 11 Mar 2025
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When LLMs are wrong they are only confidently wrong. They don't know any other way to be wrong.
This does seem to be exactly the problem. It is solvable, but I haven't seen any that do it. They should be able to calculate a confidence value based on number of corresponding sources, quality ranking of sources, and how much interpolation of data is being done vs. Straightforward regurgitation of facts.
I've been saying this for a while. They need to train it to be able to say "I don't know". They need to add questions to the dataset without enough information to solve so that it can understand what is/isn't facts vs hallucinating