Comment by Gander5739
15 hours ago
If the output can't be trusted, and you use another llm whose output can't be trusted to check the untrusted output of the first llm, then you're back where you started.
15 hours ago
If the output can't be trusted, and you use another llm whose output can't be trusted to check the untrusted output of the first llm, then you're back where you started.
Yeah this seems to me similar to how the mortgage backed security risk concentration occurred leading up to the global financial crisis. Whereby the risk from exposure to low grade / risky single mortgages was eliminated via diversification but the diversification was simply packaging all of the risky MBS’s together and in no way diversified or de-risked the entire portfolio
I'm hoping that the Big Horrible Realization comes sooner rather than later, when we have less collective damage and pain riding on it. (Plus I'd feel personally vindicated.)
I don't see it. To me it's like having e.g. 3 drunk PhDs arguing between each other to settle on truthful answers to questions.
But the problem with LLMs is that they get the facts wrong. PhDs are PhDs because they’d look it up in an authoritative source, or actually find out through research and experimentation. The whole point is that facts aren’t a matter of opinion. The only people that argue over documented, findable facts are idiots that nobody should listen to.
Unfortunately, I don;t think that can ever be fixed. For an LLM to know that it is not hallucinating about something, it must know that that statement(s) is/are true. Which it cannot infer due to Godel's Incompleteness theorems.
No you don’t get it, I asked it specifically to make sure it’s accurate
Not really. Take hallucinations for example. If they are 1 in 100 (actually they are much rarer, but for the sake of argument), then the chances that 2 LLMs or even just 2 runs of the same LLM have the same hallucination is, well, a lot less than 1 in 100.
That rests on a false-assumption that the errors are statistically independent events, and have nothing to do with the shared nature of the judges.
It's also relying on the assumption that the checking LLM only ever corrects wrong statements and never incorrectly "corrects" an already correct statement, which might not always be the case as well.
Are there any reproducible hallucinations on any of the currently available OAI/Anthropic models? I’m not aware of any.
And even if they are related - if Opus 4.8 always has a 1:100 chance of a specific hallucination - then running the same model twice does indeed dramatically reduce the odds of an error in the final output.
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