Laurence Aitchison

I'm making this grant largely on the recommendation of Marius (as he's much more involved in the evals field than me), thus only giving the minimum funding of $18.5K, but I'd be happy to see this fully funded!

The overall goal and plan here checks out to me and seem important: understanding how capable AI systems are seems very important, especially dangerous capabilities, both for forecasting risks and for coordinating agreements (like RSP-style conditional pauses). Current statistical practices seem pretty sloppy, and I can buy that there's a lot of low hanging fruit to improve it. I'm not familiar enough with the statistical theory here to say if the proposed method is the right call, but it seems fairly reasonable to me, and I'd be excited to see it explored properly!

The main concerns I see:

• A method is only useful if people actually use it - highly technical or complex methods seem less likely to get adoption. Though eval methods can be used by people outside AGI labs (eg Apollo), so there are far more chances for someone to try it. I expect the main things to help here are compelling evidence that it's useful, along with good explainers and software
• Getting data is expensive - I'm not sure how data efficient this method is compared to other things, but both designing questions and running an AI agent on them can be highly costly, and data efficiency seems super important
• To work, if I understand correctly, there must be a list of latent factors to study, and each question in the benchmark must be labelled according to which it uses? Existing benchmarks tend not to look like this, and so this will need to be addressed. My guess is that enumerating the relevant latent factors wouldn't be that hard, at least for a given dataset, and that another LLM could do a good job of labelling given access to the question and worked solution? But I'm not confident, and this is another point of failure

I have no conflicts of interest here