this post was submitted on 06 May 2025
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According to the article, it's a bigger problem for the "reasoning models" than for the older-style LLMs. Since those explicitly break problems down into multiple smaller steps, I wonder if that's creating higher hallucination rates because each step introduces the potential for errors/fabrications. Even a very small amount of "cognitive drift" might have a very large impact on the final answer if it compounds across multiple steps.
The few times I've had a local one share the "reasoning", the machine mainly just ties itself in knots over trivial nonsense for thousands of words before ignoring all of that and going with the first answer it came up with. Machine God is a Redditor.