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This take is a bit outdated now. At its core it is still true, but lots has happened to improve LLMs as fact-retrieval tools, and I think they’re generally very good at it provided you, the operator, have your head screwed on and do the work to verify.

Barry Deutsch@barrydeutsch.bsky.social

The only thing ChatGPT ever does.

4 reposts39 likes

I think it’s reasonable to expect new technology to be better than us, I can understand being frustrated when you ask it something and it comes back with total nonsense. I don’t think it’s a great defence to say “people also make mistakes.”

Reckon it will be? The pace this year has been truly incredible. I don’t know enough to know how much juice there is left to squeeze, but I haven’t seen anything truly damning. Still lots of gains to be had from model architecture, post training improvements, etc.

For example, it’s very common now for frontier models to be good at knowing when they need to go and retrieve more information. Here’s a very simple example. For higher stakes questions, you definitely want to be clicking through to the sources to make sure you’re getting good information.

But isn't that orders of magnitude more processing power and electricity use than going direct to the Met Office website yourself to get the weather forecast? With a chance it could still get it wrong. That seems like the inferior option, or is this take outdated now too?

No, this take is correct! You’re much better off going to a source you trust directly if you have one. LLMs are better when you don’t know a good source already and you want to find some, or your question is sufficiently fuzzy that you’re not sure how to ask a search engine about it.

If you relied on the model weights directly for fact retrieval then yes, you’re going to get garbage. The mental model I’ve settled on for this is that information contained in the model itself is exceptionally fuzzy and compressed. Information in the context window, though, is very clear.

I even wrote a tool to help demonstrate just how bad an idea it is to rely on the lossy compressed knowledge inside the model weights themselves. bsky.app/profile/samw...

Sam Rose@samwho.dev

Worked on this a bunch today to make it substantially faster and published it to Pypi, so you can now try it yourself much easier: ``` uvx llmwalk -p "What is 2+2?" ``` github.com/samwho/llmwalk

When I first read that take I could see where it was coming from, maybe even believed it. But it didn’t square with what I was seeing with the tools I’m using now. Love this thread and the links for further reading.