Earlier quoted context omitted.
LLMs cannot tell fact from fiction. What's commonly called hallucinations stems from it not being able to reason, the way that humans appear to be able to do, no matter that some models are called "reasoning" now. It's all the same principle: most likely token in a given position. Adding internal monologue appears to help because, by being forced to break it down (internally, or by spitballing towards the user when t…
You’re absolutely wrong! This is not how reasoning models work. Chain-of-thought did not produce reasoning models.
Doing a web search on the topic just comes up with marketing materials. Even Wikipedia's "Reasoning language model" article is mostly a list of release dates and model names, with as only relevant-sounding remark as to how these models are different: "[LLMs] can be fine-tuned on a dataset of reasoning tasks paired with example solutions and step-by-step (reasoning) traces. The fine-tuned model can then produce its own reasoning traces for new problems." It sounds like just another dataset: more examples, more training, in particular on worked examples where this "think step by step" method is being demonstrated with known-good steps and values. I don't see how that fundamentally changes how it works; you're saying such models do not predict the most likely token for a given context anymore, that there is some fundamentally different reasoning process going on somewhere?