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Selecting the Best Few with Human Evaluators: Adaptively Combining Ratings and Pairwise Comparisons

arXiv:2610.26380v1 Announce Type: new Abstract: This paper considers a decision-maker tasked with identifying the $k$ best items from a large pool of candidates, such as job applicants, LLMs, award nominees, or new product ideas. The decision-maker learns about the items' quality by soliciting huma…

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  1. 2026-10-20 04:00 · arXiv stat.ML
    Selecting the Best Few with Human Evaluators: Adaptively Combining Ratings and Pairwise Comparisons

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