A big red flag for me was that Sundar was prompting the model to report lots of facts that can be either true or false. We all saw the benchmark figures that they published and the results mostly showed marginal improvements. In other words, the issue of hallucination has not been solved. But the demo seemed to imply that it had. My conclusion was that they had mostly cherry picked instances in which the model happen…
These LLMs do not have a concept of factual correctness and are not trained/optimized as such. I find it laughable that people expect these things to act like quiz bots - this misunderstands the nature of a generative LLM entirely. It simply spits out whatever output sequence it feels is most likely to occur after your input sequence. How it defines “most likely” is the subject of much research, but to optimize for f…
I think you're slightly mischaracterising things here. It has potential to be at least slightly and possibly much better than that. This is evidenced by the fact it is much better than chance at answering "novel" questions that don't have a direct source in the training data. Why it can do it is because at a certain point, to solve the optimisation problem of "what word comes next" the least complex strategy actually becomes to start modeling principles of logic and facts connecting them. It is not in any systematic or reliable way so you can't ever guarantee when or how well it is going to apply these, but it is absolutely learning higher order patterns than simple text / pattern matching, and it is absolutely able to generalise these across topics.