[btown]

Even if you take the headline at face value (and IMO it's rather unfair)... the incredible saving grace of LLMs is that you have a plurality of BS machines, with different flavors of BS, whose outputs can be wired together.

Sure, the first-order output of today's generalist LLMs outputting one token at a time do seem to meet meet diminishing returns on factuality at approximately the level of a college freshman pulling an all-nighter. Not a great standard, that. But if you took an entire class of those tired freshmen, gave their outputs to an independent group of tired freshmen unfamiliar with the material, and told the second group to identify, in a structured manner, commonalities and discrepancies and topics they'd look up in an encyclopedia and things they'd like to escalate to a human expert on, and so on... all of a sudden, you can start to build structured knowledge about the topic, and an understanding of what is and isn't likely to be a hallucination.

One might argue that the right kind of model architecture and RLHF could bake this into the LLM itself - but you don't need to wait for that research to be brought into production to create a self-correcting system-of-systems today.