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I wish! It's not that I don't want to have a free personal assistant that can answer all my questions with zero effort of my own. And it's not like it couldn't happen, but I don't think it's inevitable or right around the corner. These are large language models, not large knowledge models. They're good at sounding like humans, not good at determining veracity. So to become consistently accurate would be a quantitative change and not just a qualitative one.

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It's true that this is an unsolved research problem, but as an ML researcher, I expect that some combination of better integration with existing information retrieval systems, a moderate amount of high-quality data, and some small but important tricks will solve it. I don't know whether the time horizon is six months, one year, or five years, but I'd be surprised if it takes longer than five. GPT-2, which was arguably the first LM that could sound human-like, is only four years old. Given the value of a trustworthy LLM, an enormous amount of research effort and resources will be directed toward this problem, and I don't think it's substantially harder than other challenges the ML community has solved over the last five years.

> These are large language models, not large knowledge models. They're good at sounding like humans, not good at determining veracity.

That sounds pretty much the same as a human.

And some humans are not even very good at sounding like humans!

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