3 ms·

I don't get the point of these benchmarks, what are they supposed to represent practically?

by epolanski 3mo ago

viccis 3mo ago
The ability of an LLM to produce something not in its training data set.
- deleted 3mo ago
  [deleted]
runarberg 3mo ago
For me this looks ideological (or even political), not practical. The theory is that LLMs are approaching general intelligence (whatever that means) and that the more generic of a task they can perform—no matter how badly—the closer we are to AGI. Specialized models can do this a lot better and for far cheaper then LLMs, but because people are so politically invested in a single statistical model being able to outperform a human on every metric (no matter how expensive the compute), then we get these ridiculous benchmarks.
- deleted 3mo ago
  [deleted]
acdha 3mo ago
The vendors like to toss around terms like “thinking” or “reasoning” to encourage prospective buyers to anthropomorphize models. These challenges are a visceral reminder that none of those marketing claims accurately describe was LLMs do: they’ll happily return things even the worst human illustrator would never hand in and make errors showing that there’s no model of the world behind anything they do. That doesn’t mean there are no ways to use them productively but rather that you should keep in mind that the same model will happily give you code or a decision with the same level of error unless you have carefully setup a QA regimen to prevent that.
- zahlman 3mo ago
  > That doesn’t mean there are no ways to use them productively but rather that you should keep in mind that the same model will happily give you code or a decision with the same level of error unless you have carefully setup a QA regimen to prevent that. The standard retort seems to be "this is also true of a large proportion of humans". But I think it's clear that there are differing patterns in how humans vs. models err on various tasks.
  - acdha 3mo ago
    Exactly right: it’s not that humans were perfect-I’ve seen too many write-offs from the big consulting companies to ever say that—but that the pattern of errors is different and any intuition you have around that will not be correct in the LLM era.
Mindless2112 3mo ago
Unless the LLM has an SVG renderer in its toolbox, it's like asking a human to draw while blindfolded. It's amazing if they can do it well, but it's practically meaningless.

I don't get the point of these benchmarks, what are they supposed to represent practically?

viccis 3mo ago

The ability of an LLM to produce something not in its training data set.

deleted 3mo ago
[deleted]

runarberg 3mo ago

For me this looks ideological (or even political), not practical. The theory is that LLMs are approaching general intelligence (whatever that means) and that the more generic of a task they can perform—no matter how badly—the closer we are to AGI. Specialized models can do this a lot better and for far cheaper then LLMs, but because people are so politically invested in a single statistical model being able to outperform a human on every metric (no matter how expensive the compute), then we get these ridiculous benchmarks.

deleted 3mo ago
[deleted]

acdha 3mo ago

The vendors like to toss around terms like “thinking” or “reasoning” to encourage prospective buyers to anthropomorphize models. These challenges are a visceral reminder that none of those marketing claims accurately describe was LLMs do: they’ll happily return things even the worst human illustrator would never hand in and make errors showing that there’s no model of the world behind anything they do. That doesn’t mean there are no ways to use them productively but rather that you should keep in mind that the same model will happily give you code or a decision with the same level of error unless you have carefully setup a QA regimen to prevent that.

zahlman 3mo ago
> That doesn’t mean there are no ways to use them productively but rather that you should keep in mind that the same model will happily give you code or a decision with the same level of error unless you have carefully setup a QA regimen to prevent that. The standard retort seems to be "this is also true of a large proportion of humans". But I think it's clear that there are differing patterns in how humans vs. models err on various tasks.
- acdha 3mo ago
  Exactly right: it’s not that humans were perfect-I’ve seen too many write-offs from the big consulting companies to ever say that—but that the pattern of errors is different and any intuition you have around that will not be correct in the LLM era.

Mindless2112 3mo ago

Unless the LLM has an SVG renderer in its toolbox, it's like asking a human to draw while blindfolded. It's amazing if they can do it well, but it's practically meaningless.