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I was going to mention this approach as well. The problem with the OP is that it has assumption bias and the entire chain is based on that assumption. It’s novel. But the original idea was to more evenly distribute scores so you can find real relevance and I think 2AFC is better. But I don’t have time to verify and post a paper about it.

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Yes our pairwise method is based entirely on 2AFC comparisons, for both intra-query and inter-query ELO calculations.

It's definitely the best if not only way to get extremely high signal, and a score assignment that actually converges the more you sample.

In terms of the "F" in 2AFC, we actually have this amusing snippet from our prompt:

> Do NOT output a score of 0.0, ensure to focus on which document is superior, and provide a negative or positive float between -1.0 and 1.0.

Nice, I use an epoch to prevent stalemate but this might be better.

It's probably because that's what we used, but nAFC has been my go-to since I first learned about it. Literally any time there's a ranking, even for dumb stuff like tier list videos on YouTube, they're too arbitrary. Ok you ranked this snack an 8/10. Based on what? And then they go back and say "actually I'm going to move that to a 7". AFC fixes all of that.

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