The authors took historical benchmark and model releases, simulated saturation by selecting items that models already solved correctly, and then tested whether protocols using only those saturated transcripts could predict performance on held-out, unsaturated items.
Transcripts from saturated benchmarks still predict model capability
Pairwise judging of solved task transcripts reduces prediction error 5.3-fold versus benchmark accuracy scores, the authors report.
Research Lab
Morio Hamada · Jehyeok Yeon · Matteo Migliarini · Justin Olive · Magda Dubois
LASR Labs · ELLIS Institute Tübingen · Max Planck Institute for Intelligent Systems · Tübingen AI Center · Sapienza University of Rome
Research Digest··2 min read
Hamada et al.
Why this paper
From Max Planck Institute for Intelligent Systems and 6 others
In one line
Correctly solved transcripts distinguish model capabilities after benchmark accuracy saturates, with pairwise judging cutting prediction error 5.27 times versus score-only estimates.
What we could check
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