Update: engineers updated the @Grok system prompt, removing a line that encouraged it to be politically incorrect when the evidence in its training data supported it.
Update: engineers updated the @Grok system prompt, removing a line that encouraged it to be politically incorrect when the evidence in its training data supported it.
Why is PC even factored in? Shouldn’t the LLM just favour evidence from the outset?
no one understands how these models work, they just throw shit at it and hope it sticks
Well thats just not true, I mean LLMs really are not extremely complicated. At the end of the day it’s just algorithmic sorting of information
So in practice any given flavor of LLM is basically like a librarian. Your librarian can be a well adjusted human or an antisemitic nutjob, but so long as they sort information and can point it out to you technically they are doing their job equally as well. The real problem doesnt begin until youve trained the librarian to recommend Mein Kampf when people ask for information about the water cycle or whatever
I think they meant people don’t know how these models work in practice. On a theoretical level they are well understood. But in practice they behave in a chaotic way (chaotic in the math sense of the word). A small change in the input can lead to wild swings in the output. So when people want to change the way the models acts by changing the system prompt, it’s basically impossible to say what change should be made to achieve the desired outcome. And often such a change doesn’t even exist, only something that’s close enough is possible. So they have to resort to trial and error, trying to tweak things like the system prompt and seeing what happens.
^-- to my knowledge, this is accurate.
System prompts are the easy but wildly unpredictable way to change LLM output, but we really can’t back-trace or debug that output, we guess at what impact the s.p. edits will have.
The problem is LLMs are programmed by biased people and trained on biased data. So “good” AI developers will attempt to mitigate that in some way.