5 ms·

It's funny to see that a lot of services are finally moving from a human-readable / plain text format towards structured protocols right at the point where we c

It's funny to see that a lot of services are finally moving from a human-readable / plain text format towards structured protocols right at the point where we can finally have LLMS parse the unstructured protocols :-)

francislavoie 2y ago
But isn't using LLM for that really expensive? Seems wasteful.
- genewitch 2y ago
  My desktop GPU can run small models at 185 tokens a second. Larger models with speculative decoding: 50t/s. With a small, finetuned model as the draft model, no, this won't take much power at all to run inference. Training, sure, but that's buy once cry once. Whether this means it's a good idea, I don't think so, but the energy usage for parsing isn't why.
  - smokel 2y ago
    A simple text parser would probably be 10,000,000 times as fast. So the statement that this won't take much power at all, is a bit of an overstatement.
  - Hercuros 2y ago
    It’s not just about the energy usage, but also purchase cost of the GPUs and opportunity cost of not using those GPUs for something more valuable (after you have bought them). Especially if you’re doing this at large scale and not just on a single desktop machine. Of course you were already saying it’s not a good idea, but I think the above definitely plays a role at scale as well.
    - SlightlyLeftPad 2y ago
      You’re right, I could be trying to get Crysis to run at 120 fps.
      - tmtvl 2y ago
        If you have spare GPU time you could donate it to projects like Folding@Home.
  - boxed 2y ago
    50 tokens per second. Compared to a quick and dirty parser written in python or even a regex? That's going to be many many orders of magnitude slower+costlier.
    - anthk 2y ago
      awk would run millions times faster, not to mention mawk and awka.
  - berkes 2y ago
    In order to make the point that > energy usage for parsing isn't why You'll need to provide actual figures and benchmark these against an actual parser. I've written parsers for larger-scale server stuff. And while I too don't have these benchmarks available, I'll dare to wager quite a lot that a dedicated parser for almost anything will outperform an LLM magnitudes. I won't be suprised if a parser written in rust uses upwards of 10k times less energy than the most efficient LLM setup today. Hell, even a sed/awk/bash monstrosity probably outperforms such an LLM hundreds of times, energy wise.
    - chgs 2y ago
      How many times would you need to parse to get an energy saving on using an lm to parse vs using an llm to write a parser, then using the parser to parse.
      - GTP 2y ago
        > using an llm to write a parser You're assuming OP needs an LLM to write a parser, since they mentions writing many during their career they probably don't need it ;)
        berkes 2y ago
        I didn't use an LLM back then. But would totally do that today (copilot). Especially since the parser(s) I wrote were rather straightforward finite state machines with stream handling in front, parallel/async tooling around it, and at the core business logic (domain). Streaming, job/thread/mutex management, FSM are all solved and clear. And I'm convinced an LLM like copilot is very good at writing code for things that have been solved. The LLM, however, would get very much in the way in the domain/business layer. Because it hasn't got the statistical body of examples to handle our case. (Parsers I wrote were a.o.: IBAN, gps-trails, user-defined-calculations (simple math formulas), and a DSL to describe hierarchies. I wrote them in Ruby, PHP, rust and perl.)
        chgs 2y ago
        I was thinking more of when a sufficiently advanced device would be able to “decide” the task would be worth using its own capabilities to write some code to tackle the problem rather than brute force. For small problems it’s not worthwhile, for large problems it is. It’s similar to choosing to manually do something vs automate it.
  - anthk 2y ago
    My Atom n270 netbook with mawk and a few lines parsing the files with a simple regex will crush down your GPU+LLM's on both time and power usage.
- genmon 2y ago
  My assumption is that models are getting cheaper, fast. So you can build now with OpenAI/Anthropic/etc and swap it out for a local or hosted model in a year. This doesn't work for all use cases but data extraction is pretty safe. Treat it like a database query -- a slow but high availability and relatively cheap call.
  - szundi 2y ago
    [dead]
  - Cthulhu_ 2y ago
    While it will become cheaper, it will never be as fast / efficient as 'just' parsing the data the old-fashioned way. It feels like using AI to do computing things instead of writing code is just like when we moved to relatively inefficient web technology for front-ends, where we needed beefier systems to get the same performance as we used to have, or when cloud computing became a thing and efficiency / speed became a factor of credit card limit instead of code efficiency. Call me a luddite but I think as software developers we should do better, reduce waste, embrace mechanical sympathy, etc. Using AI to generate some code is fine - it's just the next step in code generators that I've been using throughout all my career IMO. But using AI to do tasks that can also be done 1000x more efficiently, like parsing / processing data, is going in the wrong direction.
    - relistan 2y ago
      I know this particular problem space well. AI is a reasonable solution. WHOIS records are intentionally made to be human readable and not be machine parseable without huge effort because so many people were scraping them. So the same registrar may return records in a huge range of text formats. You can write code to handle them all if you really want to, but if you are not doing it en masse, AI is going to probably be a cheaper solution. Example: https://github.com/weppos/whois https://github.com/weppos/whois is a very solid library for whois parsing but cannot handle all servers, as they say themselves. That has fifteen + years of work on it.
      - 2y ago
- adrianmonk 2y ago
  I wouldn't use LLMs, but if I did, I would try to get the LLM to write parser code instead. If it can convert from one format to another, then it can generate test cases for the parser. Then hopefully it can use those to iterate on parser code until it passes the tests. In a sense, asking it to automate the work isn't as straightforward as asking it to do the work. But if the approach does pan out, it might be easier overall since it's probably easier to deploy generated code to production (than deploying LLMs).
- permo-w 2y ago
  deepseek API costs are quite literally pennies per million tokens
ajnin 2y ago
Well you can't really trust an LLM to give you reproducible output every time, you can't even trust it to be faithful to the input data, so that's nice to have a standard format now. And for like a millionth of the computing resources to parse it. Also Whois was barely human-readable, with the fields all over the place, missing or different from one registry to the other. A welcome change that should have come really sooner.
axegon_ 2y ago
https://deviq.com/antipatterns/shiny-toy https://deviq.com/antipatterns/shiny-toy
vrighter 2y 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.
- 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.
_ache_ 2y ago
If you job is to be a referent, to have authority. You absolutely don't want to make any error. Pretty safe isn't enough, you need to be absolutely sure that you control the output. You only have one job, don't delegate authority.
klysm 2y ago
Which world would you rather live in: * structured protocols that can be parsed by machines * unstructured protocols that are unreliably parsed by LLMs that require significant power and latency
- mmooss 2y ago
  In addition to ~determative machines and LLMs, what about humans reading the data?

francislavoie 2y ago

But isn't using LLM for that really expensive? Seems wasteful.

genewitch 2y ago
My desktop GPU can run small models at 185 tokens a second. Larger models with speculative decoding: 50t/s. With a small, finetuned model as the draft model, no, this won't take much power at all to run inference. Training, sure, but that's buy once cry once. Whether this means it's a good idea, I don't think so, but the energy usage for parsing isn't why.
- smokel 2y ago
  A simple text parser would probably be 10,000,000 times as fast. So the statement that this won't take much power at all, is a bit of an overstatement.
- Hercuros 2y ago
  It’s not just about the energy usage, but also purchase cost of the GPUs and opportunity cost of not using those GPUs for something more valuable (after you have bought them). Especially if you’re doing this at large scale and not just on a single desktop machine. Of course you were already saying it’s not a good idea, but I think the above definitely plays a role at scale as well.
  - SlightlyLeftPad 2y ago
    You’re right, I could be trying to get Crysis to run at 120 fps.
    - tmtvl 2y ago
      If you have spare GPU time you could donate it to projects like Folding@Home.
- boxed 2y ago
  50 tokens per second. Compared to a quick and dirty parser written in python or even a regex? That's going to be many many orders of magnitude slower+costlier.
  - anthk 2y ago
    awk would run millions times faster, not to mention mawk and awka.
- berkes 2y ago
  In order to make the point that > energy usage for parsing isn't why You'll need to provide actual figures and benchmark these against an actual parser. I've written parsers for larger-scale server stuff. And while I too don't have these benchmarks available, I'll dare to wager quite a lot that a dedicated parser for almost anything will outperform an LLM magnitudes. I won't be suprised if a parser written in rust uses upwards of 10k times less energy than the most efficient LLM setup today. Hell, even a sed/awk/bash monstrosity probably outperforms such an LLM hundreds of times, energy wise.
  - chgs 2y ago
    How many times would you need to parse to get an energy saving on using an lm to parse vs using an llm to write a parser, then using the parser to parse.
    - GTP 2y ago
      > using an llm to write a parser You're assuming OP needs an LLM to write a parser, since they mentions writing many during their career they probably don't need it ;)
      - berkes 2y ago
        I didn't use an LLM back then. But would totally do that today (copilot). Especially since the parser(s) I wrote were rather straightforward finite state machines with stream handling in front, parallel/async tooling around it, and at the core business logic (domain). Streaming, job/thread/mutex management, FSM are all solved and clear. And I'm convinced an LLM like copilot is very good at writing code for things that have been solved. The LLM, however, would get very much in the way in the domain/business layer. Because it hasn't got the statistical body of examples to handle our case. (Parsers I wrote were a.o.: IBAN, gps-trails, user-defined-calculations (simple math formulas), and a DSL to describe hierarchies. I wrote them in Ruby, PHP, rust and perl.)
      - chgs 2y ago
        I was thinking more of when a sufficiently advanced device would be able to “decide” the task would be worth using its own capabilities to write some code to tackle the problem rather than brute force. For small problems it’s not worthwhile, for large problems it is. It’s similar to choosing to manually do something vs automate it.
- anthk 2y ago
  My Atom n270 netbook with mawk and a few lines parsing the files with a simple regex will crush down your GPU+LLM's on both time and power usage.
genmon 2y ago
My assumption is that models are getting cheaper, fast. So you can build now with OpenAI/Anthropic/etc and swap it out for a local or hosted model in a year. This doesn't work for all use cases but data extraction is pretty safe. Treat it like a database query -- a slow but high availability and relatively cheap call.
- szundi 2y ago
  [dead]
- Cthulhu_ 2y ago
  While it will become cheaper, it will never be as fast / efficient as 'just' parsing the data the old-fashioned way. It feels like using AI to do computing things instead of writing code is just like when we moved to relatively inefficient web technology for front-ends, where we needed beefier systems to get the same performance as we used to have, or when cloud computing became a thing and efficiency / speed became a factor of credit card limit instead of code efficiency. Call me a luddite but I think as software developers we should do better, reduce waste, embrace mechanical sympathy, etc. Using AI to generate some code is fine - it's just the next step in code generators that I've been using throughout all my career IMO. But using AI to do tasks that can also be done 1000x more efficiently, like parsing / processing data, is going in the wrong direction.
  - relistan 2y ago
    I know this particular problem space well. AI is a reasonable solution. WHOIS records are intentionally made to be human readable and not be machine parseable without huge effort because so many people were scraping them. So the same registrar may return records in a huge range of text formats. You can write code to handle them all if you really want to, but if you are not doing it en masse, AI is going to probably be a cheaper solution. Example: https://github.com/weppos/whois https://github.com/weppos/whois is a very solid library for whois parsing but cannot handle all servers, as they say themselves. That has fifteen + years of work on it.
    - 2y ago
adrianmonk 2y ago
I wouldn't use LLMs, but if I did, I would try to get the LLM to write parser code instead. If it can convert from one format to another, then it can generate test cases for the parser. Then hopefully it can use those to iterate on parser code until it passes the tests. In a sense, asking it to automate the work isn't as straightforward as asking it to do the work. But if the approach does pan out, it might be easier overall since it's probably easier to deploy generated code to production (than deploying LLMs).
permo-w 2y ago
deepseek API costs are quite literally pennies per million tokens

ajnin 2y ago

Well you can't really trust an LLM to give you reproducible output every time, you can't even trust it to be faithful to the input data, so that's nice to have a standard format now. And for like a millionth of the computing resources to parse it. Also Whois was barely human-readable, with the fields all over the place, missing or different from one registry to the other. A welcome change that should have come really sooner.

axegon_ 2y ago

https://deviq.com/antipatterns/shiny-toy https://deviq.com/antipatterns/shiny-toy

vrighter 2y 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.

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.

_ache_ 2y ago

If you job is to be a referent, to have authority. You absolutely don't want to make any error. Pretty safe isn't enough, you need to be absolutely sure that you control the output. You only have one job, don't delegate authority.

klysm 2y ago

Which world would you rather live in: * structured protocols that can be parsed by machines * unstructured protocols that are unreliably parsed by LLMs that require significant power and latency

mmooss 2y ago
In addition to ~determative machines and LLMs, what about humans reading the data?