alt Hacker NewsFixing unreliable agents by adding more unreliable agents. It's a shame we can't do something about the tendency of LLMs to give more weight to their weights than to the data they're analysing.
I guess it all comes back to the fact that they don't have a clear division between data they have learned about the world, instructions and data under analysis.
You can ask them only to consider the 5 facts in front of them, but they won't do that, and cannot do that.
> Fixing unreliable agents by adding more unreliable agents.
Depending on how you combine imperfect systems, you can lower or increase error rates.
> data they have learned about the world, instructions and data under analysis.
I don't think there really is a clear distinction between these things though.
And I don't mean this in a gotcha way - these things apply to people too. Not that that means they're like people, but we have successfully built enormous systems out of fallible people, and other imperfect things.
The goal is rarely "eliminate all errors" but "have a thing that's useful, given a possibility of things going wrong".