Comment by TeMPOraL
2 years ago
Precisely this. People dismiss utility of LLMs because they don't give 100% reliability, without considering the basic facts that:
- LLMs != ChatGPT interface, they don't need to be run in isolation, nor do they need to do everything end-to-end.
- There are no 100% reliable systems - neither technological nor social. Voltages fluctuate, radiation flips bit, humans confabulate just as much if not worse than LLMs, etc.
- We create reliability from unreliable systems.
LLMs aren't some magic unreliability pixie dust that makes everything they touch beyond repair. They're just another system with bounded reliability, and can be worked into larger systems just like anything else, and total reliability can be improved through this.
EDIT: In fact, my example with probabilistic primality tests is bad because those tests are too nice - they let us compute tight bounds on the error rate in advance. LLMs are not like that. But then, a lot of systems we rely in our daily lives also have this property - their reliability is established empirically, i.e. we improve them until they work reliably enough, and then we hope they'll keep on working, and deal with random failures when they occur. So that's nothing new, either.
No, LLMs do not have "bounded reliability". All reliability figures for LLMs are based upon empirical observation in specific contexts using artificial benchmarks. As they say in finance, "past performance is not indicative of future results".
Saying LLMs are no worse than random bit flips is, again, an unjustified comparison. We can control bit errors with ECC, we cannot control the output of an LLM except to shackle it into uselessness.
I said bounded. I didn't say how tight. But all of science is about bounding empirical observations, so this is nothing new - nor is relying on systems with empirically established failure rates, which is a good chunk of what engineering is about.
The number of 9s that can be assigned to these "bounds" currently is zero. They are not even 90% reliable. And there is no straightforward way to get to 90%, never mind 95%, 99%, etc. The sliding scale of reliability you originally presented just does not exist.
Yeah, sure, we can hypothetically engineer a system that tolerates a key step in the process which has, say, a 30% chance of being wrong, including a 10% chance of being dangerously wrong (appears correct but is broken in subtle ways), and a 5% chance of being batshit insane, but why would we? The amount of training, vetting, and supervision of human operators necessary to make a working process here immediately raises the question of whether the machine serves man or the other way around.
The best uses of an LLM are those where engineering levels of precision are neither required nor useful.
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Finance is an excellent analogy. Relying on LLM output is similar to relying on the stock market. You might come out ahead but it's always a gamble and the lower bound is always catastrophic failure.