Benchmarks can get costly to run- you can reach out to frontier model creators to try and get them to give you free credits, but usually they'll only agree to that once your benchmark is pretty popular.

so basically they know requests using your API key should be treated with care?

they could but you can also have some trust in anthropic to have some integrity there, these are earnest people.

"trust but verify" ofc . https://latent.space/p/artificialanalysis do api keys but also mystery shopper checks

> these are earnest people.

I agree.

I'll also add that when my startup got acquired into a very large, well-known valley giant with a sterling rep for integrity and I ended up as a senior executive - over time I got a first-hand education on the myriad ways genuinely well-intentioned people can still end up being the responsible party(s) presiding over a system doing net wrong things. All with no individual ever meaning to or even consciously knowing.

It's hard to explain and I probably wouldn't have believed myself before I saw and experienced it. I'm pretty sure I ended up moving on before I compromised myself but preventing that required constant vigilance and led to some inter-personal and 'official' friction. Standing against an overwhelming organizational tide is stressful and never leads to popularity or promotion.

Someone posted this here the other day and it uses _Demons_ to discuss exactly your point.

https://possessedmachines.com/

Wow. Only one page in and already bookmarked to absorb later. Thanks for the link.

That's why we're setting up adversarial benchmarks to test if they are doing the thing they promised not to do, because we totally trust them.

The last thing a proper benchmark should do is reveal it's own API key.

That's a good thought I hadn't had, actually.

IMO it should need a third party running the LLM anyway. Otherwise the evaluated company could notice they're receiving the same requests daily and discover benchmarking that way.

But that's removing a component that's critical for the test. We as users/benchmark consumers care that the service as provided by Anthropic/OpenAI/Google is consistent over time given the same model/prompt/context

Might as well have the free tokens, then, especially if it is an open benchmark they are already aware of. If they want to game it they cannot be stopped from doing so when it's on their infra.

With the insane valuations and actual revenue at stake, benchmarkers should assume they're assessing in an adversarial environment. Whether from intentional gaming, training to the test, or simply from prioritizing things likely to make results look better, targeting benchmarks will almost certainly happen.

We already know large graphics card manufacturers tuned their drivers to recognize specific gaming benchmarks. Then when that was busted, they implemented detecting benchmarking-like behavior. And the money at stake in consumer gaming was comparatively tiny compared to current AI valuations. The cat-and-mouse cycle of measure vs counter-measure won't stop and should be a standard part of developing and administering benchmark services.

Beyond hardening against adversarial gaming, benchmarkers bear a longer term burden too. Per Goodhart's Law, it's inevitable good benchmarks will become targets. The challenge is the industry will increasingly target performing well on leading benchmarks, both because it drives revenue but also because it's far clearer than trying to glean from imprecise surveys and fuzzy metrics what helps average users most. To the extent benchmarks become a proxy for reality, they'll bear the burden of continuously re-calibrating their workloads to accurately reflect reality as user's needs evolve.

yes I reached out to them but as you say it's a chicken-and-egg problem.

Thanks!