5 ms·

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.

TeMPOraL 2y ago
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_tests https://en.wikipedia.org/wiki/Primality_test#Probabilistic_t...
- kbolino 2y ago
  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.
  - dcow 2y ago
    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.
    - wizzwizz4 2y ago
      There's no such thing as a perfectly-watertight roof, therefore there's no qualitative difference between fixing the roof and buying a bigger bucket.
    - TeMPOraL 2y 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.
      - kbolino 2y ago
        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.
        TeMPOraL 2y ago
        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.
  - waterhouse 2y ago
    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.
  - billbrown 2y ago
    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.
robotresearcher 2y ago
> Which works perfectly ... on conformant inputs, when it has no bugs.
- kibwen 2y ago
  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.
derefr 2y ago
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.)

TeMPOraL 2y ago

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_tests https://en.wikipedia.org/wiki/Primality_test#Probabilistic_t...

kbolino 2y ago
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.
- dcow 2y ago
  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.
  - wizzwizz4 2y ago
    There's no such thing as a perfectly-watertight roof, therefore there's no qualitative difference between fixing the roof and buying a bigger bucket.
  - TeMPOraL 2y 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.
    - kbolino 2y ago
      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.
      - TeMPOraL 2y ago
        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.
- waterhouse 2y ago
  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.
- billbrown 2y ago
  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.

robotresearcher 2y ago

> Which works perfectly ... on conformant inputs, when it has no bugs.

kibwen 2y ago
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.

derefr 2y ago

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.)