Adaptive human evaluations improve selecting top items from large pools
Selecting the Best Few with Human Evaluators: Adaptively Combining Ratings and Pairwise Comparisons
Machine Learning
Summary
Choosing the best items from a big list, like top job candidates or product ideas, can be costly when relying on people to evaluate each one. This paper studies how to get the most useful feedback while using a limited budget for evaluations. The authors propose a new method that mixes two types of human assessments—ratings and comparisons—to efficiently narrow down the top selections. Their approach starts with ratings to get a rough idea and then uses pairwise comparisons to fine-tune the choices. They show through simulations that this method works better than existing approaches.
What this means in practice
- •For hiring managers: Improve candidate selection by adaptively combining ratings and comparisons to efficiently identify the best applicants.
- •For product development teams: Optimize the process of selecting top product ideas using limited human feedback that balances quick ratings and detailed comparisons.
Authors
Motti Goldberger, Nils Rudi
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 human evaluations. Each evaluation can be costly, so the decision-maker aims to use the limited evaluation budget efficiently by allocating evaluations adaptively. A key consideration is the form of evaluation to solicit, which should be something evaluators are comfortable providing. Two popular forms are ratings and pairwise comparisons, and we allow for both, as they may be especially useful in combination given the limited budget. Ratings can be used early on to get a coarse picture of which items are good and which are not. As that picture forms, comparisons can refine the ordering of the good items, narrowing in on the ultimate top-$k$ selection. To solve the allocation problem, we develop an adaptive algorithm built from two allocation policies. The first, our primary contribution, is based on a surrogate objective that is naturally forward-looking, valuing evaluations that help inform what to evaluate in future rounds, not just those that directly improve the selection decision. The decision rule is interpretable, and we prove that it is asymptotically consistent. Late in the evaluation process, when being forward-looking is less productive, we construct a myopic policy and a criterion for switching to it. In simulation, the method gives insight into how to combine these two types of evaluations, and beats the (limited) existing benchmarks.