FLIPS identifies LLM instances with 96% closed-set and 90% open-set accuracy by exploiting biases in generated binary random sequences across 237 instances.
arXiv preprint arXiv:2506.07673 , year=
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The benchmark score matrix of 84 models on 133 tasks is approximately rank-2; BenchPress recovers held-out scores to within 4.6 points and identifies 5-benchmark subsets that predict the full scorecard to within 3.93-4.55 points.
Maps common low-compute research strategies for foundation models onto statistical, internal, external, and construct validity threats via a causal-inference lens.
SIREN corrects winner's curse bias in adaptive LLM benchmarking via selection-aware repeated splits and bootstrap for valid procedure-level confidence intervals.
Factorized Active Querying (FAQ) provides up to 5 times more effective samples for LLM accuracy estimation by using Bayesian factor models and adaptive querying under a fixed budget with guaranteed coverage.
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