Any model with a good score on this benchmark would have a good claim on superhuman abilities. Humans are pretty terrible at being thrown a long policy document and being expected to follow it

And while we shouldn't anthropomorphize these models too much, I wouldn't be surprised if many of the core reasons for failures are similar. Working memory is a limited resource; you can only focus on so many things at once; reasoning depth is limited; and many real-world policies are not actually meant to be implemented in the same way they are written and have insufficient specification of edge cases

With humans, we usually do the equivalent of RLHF, both via "training" with simulated cases, and via feedback while on the job. You would never hand a newbie a 124 page policy document and expect them to correctly apply it on the first task, or to do it reliably in the first month

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batshit_beaver an hour ago | parent | next [-]

The challenge with comparing these things to humans, is that humans learn. A newbie might not respect your organization’s set of policies on day one, but what about 3 months in? Or 3 years? Meanwhile there’s still no reasonable mechanism for automatically fine tuning LLMs or adjusting their harnesses to make them better at completing your organization’s objectives more successfully. They’re still overwhelmingly governed by the shared weights and harness policies found to be successful for the average case.

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kridsdale1 an hour ago | parent [-]

Models learn. It just costs $10B and 1 year to do what a human does every night.

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wongarsu 39 minutes ago | parent [-]

LoRAs or even full fine-tunes would be much cheaper than that, and with some investment in the right infra could be updated regularly. And at least LoRAs can be swapped in and out cheaply, making them usable in large-scale inference providers. But there seems to be limited appetite in offering this. Both Anthropic and OpenAI no longer offer fine tuning for current models

	▲	8note 10 minutes ago \| parent [-]
		does lora do a good job at teaching the model new things that werent in the training data? without trillions of examples of following instructions at a million context length, im not convinced the behaviour is in the weights to begin with

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loremium 2 hours ago | parent | prev | next [-]

isn't it because there are too many contradictions and ambiguity? the reason it works for humans is because we don't apply everything at once either.

	▲	AnimalMuppet an hour ago \| parent [-]
		No, it's more than that. We can't remember everything. I hired, say, two years ago; as part of my onboarding process I had to read a bunch of policy and procedure documents, which were full of stuff that I didn't understand because I wasn't really in the context yet. So at the time, to me, those documents were full of arbitrary text that I didn't really understand. Some of it was rules that I had to follow, but at the time I didn't understand why, so it's just arbitrary rules. How many arbitrary rules can you memorize? Do you even remember them two years later? If you do, then we can get to your statement. And your statement is true. Humans do not run every action through a memorized list of rules, to see if any of them block the action. We don't. We're not going to, either, no matter how badly the policy manual writers want us to.

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ActionHank 3 hours ago | parent | prev [-]

That's a great comparison, human vs ai on a wall of text.

The problem is that it doesn't fit the sales pitch of LLMs and agents - humanlike or better, repeatably, 24/7, for a fraction of the price, you just need to make sure that you give it all the rules.

Unfortunately we can't really have a meaningful conversation until the money vampires have left so we will need to reschedule this until after the bubble.