Nothing of the sort. I'm trying to understand why anyone cares about formal verifiability in this context, since it's not something we rely on when asking humans to answer questions for us. We evaluate any answer we get without such mathematical proofs, and instead simply judge the answer we're given on its fit and usefulness.

Anyone who doubts the usefulness of even these nascent LLMs is fooling themselves. The proof is in the pudding, they already do a great job, even with all their obvious limitations.

> since it's not something we rely on when asking humans to answer questions for us

Because we interact with computers (which includes LLMs) differently than we do with humans and we hold them to higher standards

Ironically, Google played a large part in this, delivering high quality results to us with ease for many years. At one point Google was the standard for finding high quality information

Shrug. Seems like clutching pearls to me. People seem to have an emotional reaction and obsess on the aspects that differentiate human cognition from LLMs. But that is a lot of wasted energy.

To the extent that anyone avoids employing these technologies, they will be at a disadvantage to those who do; because these tools just work. Already. Today.

There isn't even room for debate on that issue. Again, the proof is in the pudding. These systems are already successfully, usefully, and correctly answering millions of questions a day. They have failure modes where they produce substandard or even flat out incorrect answers too. They're far from perfect, but they're still incredible tools, even without waiting for the improvements that are sure to come.

The reason verifiability is important is because humans can be incentivized to be truthful and factual. We know we lie, but we also know we can produce verifiable information, and we prefer this to lies, so when it matters, we make the cost to lying high enough that we can reasonably expect that they will not try to deceive (for example by committing perjury, or fabricating research data). We know it still happens, but it’s not widespread and we can adjust the rules, definitions and cost to adapt.

An LLM does not have such real world limitations. It will hallucinate nonstop and then create layers of gaslighting explanations to its hallucinations. The problem is that you absolutely must be a domain expert at the LLM’s topic or always go find the facts elsewhere to verify (then why use an LLM?).

So a company like Google using an LLM, is not providing information, it’s doing the opposite. It is making it more difficult and time consuming to find information. But it is then hiding their responsibility behind the model. “We didn’t present bad info, our model did, we’re sorry it told you to turn your recipe into poison…models amirite?”

A human doing that could likely face some consequences.