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stat.ML2026
FUSE: Ensembling Verifiers with Zero Labeled Data
Joonhyuk Lee, Virginia Ma, Sarah Zhao +4
Verification of model outputs is rapidly emerging as a key primitive for both training and real-world deployment of large language models (LLMs). In practice, this often involves u…
stat.ML2026
Efficient Evaluation of LLM Performance with Statistical Guarantees
Skyler Wu, Yash Nair, Emmanuel J. Candès
Exhaustively evaluating many large language models (LLMs) on a large suite of benchmarks is expensive. We cast benchmarking as finite-population inference and, under a fixed query…