Comment by vrighter
2 years ago
we can't ever have LLMs reliably parse any form of data. You know what can parse it perfectly though? A parser. Which works perfectly, and consistently.
2 years ago
we can't ever have LLMs reliably parse any form of data. You know what can parse it perfectly though? A parser. Which works perfectly, and consistently.
Except that the whole problem of WHOIS, that RDAP is solving, is that a WHOIS response an unstructured plaintext response, that's entirely arbitrarily formatted according to the whims of the TLD manager.
Ever tried to parse WHOIS data? You literally have to write a parser per TLD.
And things get even more stupid when you start talking about WHOIS records for IP ranges. Then you have to write a parser per IP-range delegation — starting at IANA, and working recursively, all the way down to the individual ASN. Where you have no idea how many delegating parties are going to be involved — and so get their own step in the chain, formatted however they wish — for any given IP address. (Ask me how I know.)
> Which works perfectly
... on conformant inputs, when it has no bugs.
On non-conformant inputs, a parser will barf and yell at you, which is exactly what you want.
On non-conformant inputs, there's absolutely no telling what an LLM will do, which is precisely the problem. It might barf, or it might blissfully continue, and even if the input was right you couldn't remotely trust it to regurgitate the input verbatim.
As for bugs, it is at least theoretically possible to write a parser with no bugs, whereas an LLM is fundamentally probabilistic.
Of course we can. Reliability is a spectrum, not a binary state. You can push it up however high you like, and stop somewhere between "we don't care about error rate this low" and "error rate is so low it's unlikely to show in practice".
It's not like this is a new concept. There are plenty of algorithms we've been using for decades that are only statistically correct. A perfect example of this is efficient primality testing, which is probabilistic in nature[0], but you can easily make the probability of error as small as "unlikely to happen before heat death of the universe".
--
[0] - https://en.wikipedia.org/wiki/Primality_test#Probabilistic_t...
There are two problems with this comparison. First, probabilistic prime generation has a mathematically proven lower bound that improves with iteration. There is no comparably robust tuning parameter with an LLM. You can use a different model, you can use a bigger variant of the same model, etc., but these all have empirically determined and contextually sensitive reliability levels that are not otherwise tunable. Second, the prime generation function will always give you an integer, and never an apple, or a bicycle, or a phantasm. LLMs regurgitate and hallucinate, which means that a simple error rate is not the only metric that matters. One must also consider how egregiously wrong and even nonsensical the errors can be.
I think the better statement is that, if, say, you're running the Miller-Rabin test 10 times, you can be confident that an error in one test is uncorrelated with an error in the next test, so it's easy to dial up the accuracy as close to 1 as desired. Whereas with an LLM, correlated errors seem much more likely; if it failed three times parsing the same piece of data, I would have no confidence that the 4th-10th times would have the same accuracy rate as on a fresh piece of data. LLMs seem much more like the Fermat primality test, except that their "Carmichael numbers" are a lot more common.
I compare LLMs to a door with a slot where you put a piece of paper with a request on it and you get something back related to that request. If it's the same every time, great. But it might be different or completely wrong. You don't know what goes on behind the door and measuring the error rate tells you little predictive.
The general point is not that the feature currently exists to dial down the LLM parse error rate, it’s that the abstract argument “we can’t use LLMs because they aren't perfect” isn’t a realistic argument in the first place. You’re probably reading this on hardware that _probably_ shows you the correct text most all of the time but isn’t guaranteed to.
9 replies →