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When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

arxiv.org

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Re: When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

#31

Post by runarberg »

adrianN wrote: post #8
otterdude wrote: post #2

This seems to be the end of the road for LLM's. There's only so much accuracy on a highly non-linear space you can get from a regression. If the Pareto rule is any indicator, 80% of results come from 20% of causes. It seems that we have alot more to learn about intelligence. I am reminded of a statement on truth from an ancient philosopher, this sentiment seems to be exactly the opposite of the LLM training paradigm…

I really don’t think we know enough about what „intelligence“ is or how LLMs actually work to confidently say that this is the end of the road for LLM.

We know plenty about human cognition, and we know everything about how LLMs work. True we don’t know anything about intelligence but that is because “intelligence” is it self a fraught and vague term, and we haven’t (and perhaps never will) settled on what it means exactly.

Re: When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

#32

Post by scotty79 »

otterdude wrote: post #23
astro1234 wrote: post #15

Earlier quoted context omitted.

I work in AI evaluation, lots of problems and leakage is an issue as is ecological validity, but they definitely do not explain the progress we see. I think Epoch has the best analysis I’ve seen on evaluation trends; they use IRT to basically model a variety of benchmark difficulties, and then model a capability parameter for each model. This is as robust a sort of “meta-study” of evaluations as I’ve seen and the tre…

Benchmarks saturate around 80-90%? This is not "Acing" a test, this is hitting a wall.

Even on very small tests a fraction of questions might have wrong answers in the key.

If models can't get more than 90% of the benchmark right I think it's a strong indication that they were not trained on the answers and that benchmark itself is messy enough that <10% desired answers might be wrong or misleading.

Re: When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

#33

Post by runarberg »

warkdarrior wrote: post #29
tsunamifury wrote: post #20

Earlier quoted context omitted.

I'm sorry, we know exactly how LLMs work, this myth that we "dont know how they work" was perpetuated by executives that dont know how they work. We know exactly how attention layers work and how they produce the next word as well as draw them from larger feature spaces.

If you know all this, can you explain how these models produce advanced mathematical proofs? (as recently done by OpenAI, for example) I tried to generate the next word to the best of my ability, starting with a mathematical problem, but I did not create a valid proof. How do these LLMs work when they create math proofs to problems not yet solved?

You don’t have the computational ability to process as many calculations as a datacenter. You can hardly transpose a 5×5 matrix in your mind, so you won’t be able to do what datacenters do.

This is like saying we don‘t know how a car works because a car can beat the best human athletes in 100 meter dash.

Re: When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

#34

Post by otterdude »

logicchains wrote: post #12
otterdude wrote: post #2

This seems to be the end of the road for LLM's. There's only so much accuracy on a highly non-linear space you can get from a regression. If the Pareto rule is any indicator, 80% of results come from 20% of causes. It seems that we have alot more to learn about intelligence. I am reminded of a statement on truth from an ancient philosopher, this sentiment seems to be exactly the opposite of the LLM training paradigm…

>This seems to be the end of the road for LLM's. There's only so much accuracy on a highly non-linear space you can get from a regression. It's just inadequate benchmarks. Anyone who has used Fable for anything particularly difficult will have seen that it's miles ahead of Opus 5.0, yet the majority of benchmarks are completely unable to capture this.

If most models were getting 100% on the test it would be an inadequate benchmarks.

What were seeing is all models failing to ace these tests.

"Benchmark Saturation" is term that promotes lowering the bar.

Re: When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

#35

Post by runarberg »

heaney-555 wrote: post #18
otterdude wrote: post #2

This seems to be the end of the road for LLM's. There's only so much accuracy on a highly non-linear space you can get from a regression. If the Pareto rule is any indicator, 80% of results come from 20% of causes. It seems that we have alot more to learn about intelligence. I am reminded of a statement on truth from an ancient philosopher, this sentiment seems to be exactly the opposite of the LLM training paradigm…

>This seems to be the end of the road for LLM's This is an amazingly ignorant thing to say given the current pace of progress.

And sales of disco records were up 400% for the year ending 1976. If these trends continue...

Re: When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

#37

Post by ForHackernews »

warkdarrior wrote: post #29
tsunamifury wrote: post #20

Earlier quoted context omitted.

I'm sorry, we know exactly how LLMs work, this myth that we "dont know how they work" was perpetuated by executives that dont know how they work. We know exactly how attention layers work and how they produce the next word as well as draw them from larger feature spaces.

If you know all this, can you explain how these models produce advanced mathematical proofs? (as recently done by OpenAI, for example) I tried to generate the next word to the best of my ability, starting with a mathematical problem, but I did not create a valid proof. How do these LLMs work when they create math proofs to problems not yet solved?

You can see how an LLM works here https://bbycroft.net/llm they are not magic.

Re: When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

#39

Post by [deleted] »

otterdude wrote: post #2

This seems to be the end of the road for LLM's. There's only so much accuracy on a highly non-linear space you can get from a regression. If the Pareto rule is any indicator, 80% of results come from 20% of causes. It seems that we have alot more to learn about intelligence. I am reminded of a statement on truth from an ancient philosopher, this sentiment seems to be exactly the opposite of the LLM training paradigm…

[deleted]

Re: When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

#40

Post by antisthenes »

otterdude wrote: post #2

This seems to be the end of the road for LLM's. There's only so much accuracy on a highly non-linear space you can get from a regression. If the Pareto rule is any indicator, 80% of results come from 20% of causes. It seems that we have alot more to learn about intelligence. I am reminded of a statement on truth from an ancient philosopher, this sentiment seems to be exactly the opposite of the LLM training paradigm…

It's also important not to put too much faith into ancient sayings and aphorisms.

As a civilization, we are currently brushing up against the physics of efficiency. In many areas we have achieved close to what is theoretically possible, based on physics.

Such was not the case for the majority of human existence.

The body of research a.k.a. "writings of the ancients" is now insurmountably higher than it would have been during the time of ibn al-Haytham, when any kind of writing at all was scarce and literacy was low.

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