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New algorithm combines human ratings and pairwise comparisons for item selection

By PulseAugur Editorial · [1 sources] ·

A new research paper proposes an adaptive algorithm for selecting the top $k$ items from a large pool using human evaluations. The algorithm intelligently combines ratings and pairwise comparisons, optimizing the use of limited evaluation budgets. It features a forward-looking allocation policy that prioritizes evaluations informing future rounds, with a switch to a myopic policy when nearing the final selection. AI

IMPACT This research could improve the efficiency of selecting top candidates in various AI-related applications, such as LLM evaluation or product idea selection.

RANK_REASON Academic paper detailing a new algorithm for item selection using human evaluations. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New algorithm combines human ratings and pairwise comparisons for item selection

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Academic paper detailing a new algorithm for item selection using human evaluations. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Motti Goldberger, Nils Rudi ·

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

    arXiv:2610.26380v2 Announce Type: replace-cross 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 item…

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