5 ms·

> not-entirely-useless but still quite crappy 15 months ago, general-purpose LLMs that have not been specifically trained on legal reasoning could score better

> not-entirely-useless but still quite crappy 15 months ago, general-purpose LLMs that have not been specifically trained on legal reasoning could score better than 90% of humans on the multistate bar exam, and these are humans who actually completed law school. General-purpose LLMs get similar results in medicine, and when the models are fine-tuned for medical diagnosis they're even better. And that was more than a year ago. Those who have seen current models not yet released tell us they'll make the current state of the art look like toys. Progress is still tracking the steep part of the S-curve, and there's no indication that they're near the top yet.

flimsypremise 2y ago
It's not super surprising that LLMs perform well on standardized tests, given that they have a lot of standardized test related text in their training data. There are a lot of claims out there about the zero-shot ability of LLMs, and very little specific research to back it up. Until now that is.
- MacsHeadroom 2y ago
  This paper is about CLIP not LLMs and does not generalize to LLM architectures.
  - elicksaur 2y ago
    Why not?
crote 2y ago
> Progress is still tracking the steep part of the S-curve, and there's no indication that they're near the top yet. If I understand it correctly, that seems to be exactly what this paper is suggesting. Scoring high on the bar exam is pretty trivial for AI - the data needed for that is fairly generic and widely available on the internet. It requires you to demonstrate a relatively basic understanding of the concepts by answering a bunch of multiple-choice answers. If anything, I'd expect AI to have a perfect score. Like I said, such an AI is not entirely useless. You can replace quite a few legal assistants with that, and I bet it could be used to create first drafts or to expand a core concept into a full legal argument. There is plenty of money to be made there, and it's going to make an awful lot of people jobless. But that's just replacing more-trivial jobs with automation, it doesn't add anything novel to society. On the other hand, the actual difficult work involves being able to come up with completely novel concepts, and being able to expand upon some obscure but crucial stuff few people have ever heard about. Current models simply aren't capable of that, and the results achieved here with multimodal models suggests that they never will. We risk getting stuck with models which can do some trivial work, but silently produce complete garbage when you ask them to do anything providing substantial value.
- pas 2y ago
  what is novel? what if you couple a random text generator with a picture generator, isn't each new picture novel? if we could send all artists to work in the healthcare mines we might discover new treatment for various ailments. is that not novel? is that too indirect? increasing "economic surplus with externalities factored in" is good for society even if it's not novel. arguably philosophers come up with novel stuff all the time, and ... > Current models simply aren't capable of that, and the results achieved here with multimodal models suggests that they never will. can you elaborate on this a bit please?
  - yladiz 2y ago
    > if we could send all artists to work in the healthcare mines we might discover new treatment for various ailments. is that not novel? is that too indirect? You do realize that artists are artists because they want to be, and even if AI can create art, artists will still be artists, right? They won't just be like, "Oh, AI is good at generating art now? Whelp time to go become a doctor."
    - chefandy 2y ago
      Wait, you're saying people's brains aren't fungible compute cycles? Casually discussing people's lives as top-down resource allocation problems is one of the things I hate most about how "tech optimists" discuss this.
Imnimo 2y ago
I think this says more about the benchmark than the capabilities of the model. If it were the case that 90th percentile performance on the bar exam mean that a model was a 90th percentile lawyer, and we've had these models for 15 months (in fact longer), where are all the LLM lawyers? The lesson here is that a test designed for humans may not be equally representative of capabilities when given to an LLM.
- pas 2y ago
  nah, it's probably an okay-ish benchmark for humans, but it's just that, a filter to weed out those who can't learn hundreds of pages of legal trivia. the model is great at this, because the training set is full of this stuff. the questions are static and simple. (the actual text of the questions change, of course, but the format and the answers are from a fixed set. and the LLM doesn't need to generate text basically, just one token. A B C or D ... that said I'm curious how it's administered to the LLMs and how much that influences their performance.)
elicksaur 2y ago
> 15 months ago, general-purpose LLMs that have not been specifically trained on legal reasoning could score better than 90% of humans on the multistate bar exam The claims of similar performance on coding problems were shown to be due to contamination of the training data on the tested problems. It did abysmal on problems made public after the model training cutoff. I don’t think anyone has tested contamination for the MBE claims, but I would lean toward assuming the same issue exists for that assessment until proven otherwise.
- coredog64 2y ago
  You can see similar (poor) results if you give it a slightly tweaked puzzle that it’s seen before. The most recent example on Twitter was the farmer with the animals and the boat. If there are no constraints, it will give you an answer based on the tricks for the original unless you harass it.

flimsypremise 2y ago

It's not super surprising that LLMs perform well on standardized tests, given that they have a lot of standardized test related text in their training data. There are a lot of claims out there about the zero-shot ability of LLMs, and very little specific research to back it up. Until now that is.

MacsHeadroom 2y ago
This paper is about CLIP not LLMs and does not generalize to LLM architectures.
- elicksaur 2y ago
  Why not?

crote 2y ago

> Progress is still tracking the steep part of the S-curve, and there's no indication that they're near the top yet. If I understand it correctly, that seems to be exactly what this paper is suggesting. Scoring high on the bar exam is pretty trivial for AI - the data needed for that is fairly generic and widely available on the internet. It requires you to demonstrate a relatively basic understanding of the concepts by answering a bunch of multiple-choice answers. If anything, I'd expect AI to have a perfect score. Like I said, such an AI is not entirely useless. You can replace quite a few legal assistants with that, and I bet it could be used to create first drafts or to expand a core concept into a full legal argument. There is plenty of money to be made there, and it's going to make an awful lot of people jobless. But that's just replacing more-trivial jobs with automation, it doesn't add anything novel to society. On the other hand, the actual difficult work involves being able to come up with completely novel concepts, and being able to expand upon some obscure but crucial stuff few people have ever heard about. Current models simply aren't capable of that, and the results achieved here with multimodal models suggests that they never will. We risk getting stuck with models which can do some trivial work, but silently produce complete garbage when you ask them to do anything providing substantial value.

pas 2y ago
what is novel? what if you couple a random text generator with a picture generator, isn't each new picture novel? if we could send all artists to work in the healthcare mines we might discover new treatment for various ailments. is that not novel? is that too indirect? increasing "economic surplus with externalities factored in" is good for society even if it's not novel. arguably philosophers come up with novel stuff all the time, and ... > Current models simply aren't capable of that, and the results achieved here with multimodal models suggests that they never will. can you elaborate on this a bit please?
- yladiz 2y ago
  > if we could send all artists to work in the healthcare mines we might discover new treatment for various ailments. is that not novel? is that too indirect? You do realize that artists are artists because they want to be, and even if AI can create art, artists will still be artists, right? They won't just be like, "Oh, AI is good at generating art now? Whelp time to go become a doctor."
  - chefandy 2y ago
    Wait, you're saying people's brains aren't fungible compute cycles? Casually discussing people's lives as top-down resource allocation problems is one of the things I hate most about how "tech optimists" discuss this.

Imnimo 2y ago

I think this says more about the benchmark than the capabilities of the model. If it were the case that 90th percentile performance on the bar exam mean that a model was a 90th percentile lawyer, and we've had these models for 15 months (in fact longer), where are all the LLM lawyers? The lesson here is that a test designed for humans may not be equally representative of capabilities when given to an LLM.

pas 2y ago
nah, it's probably an okay-ish benchmark for humans, but it's just that, a filter to weed out those who can't learn hundreds of pages of legal trivia. the model is great at this, because the training set is full of this stuff. the questions are static and simple. (the actual text of the questions change, of course, but the format and the answers are from a fixed set. and the LLM doesn't need to generate text basically, just one token. A B C or D ... that said I'm curious how it's administered to the LLMs and how much that influences their performance.)

elicksaur 2y ago

> 15 months ago, general-purpose LLMs that have not been specifically trained on legal reasoning could score better than 90% of humans on the multistate bar exam The claims of similar performance on coding problems were shown to be due to contamination of the training data on the tested problems. It did abysmal on problems made public after the model training cutoff. I don’t think anyone has tested contamination for the MBE claims, but I would lean toward assuming the same issue exists for that assessment until proven otherwise.

coredog64 2y ago
You can see similar (poor) results if you give it a slightly tweaked puzzle that it’s seen before. The most recent example on Twitter was the farmer with the animals and the boat. If there are no constraints, it will give you an answer based on the tricks for the original unless you harass it.