Learning What Evaluators Value: A Reliable Approach to Modeling Evaluator Preferences

2026-05-15

AI Summary

This work addresses the critical challenge of accurately modeling preference functions that aggregate multidimensional criteria into holistic judgments in settings such as admissions and medical diagnosis. Departing from conventional assumptions of linearity or strong structural forms, the paper proposes the first robust nonparametric learning algorithm that achieves optimal performance without requiring any prior knowledge of the preference structure, assuming only monotonic non-decreasing behavior across each criterion. Theoretical analysis demonstrates the severe consequences of common model misspecifications, while experiments on both synthetic and real-world data confirm that the method maintains statistical efficiency under linear preferences and reliably recovers true evaluator preferences in general cases. Notably, the approach effectively uncovers key behavioral differences between human evaluators and large language models in their assessment strategies.

Abstract

In many applications, human and LLM evaluators use assessments of relevant criteria to create an overall evaluation for an item or individual. For example, in admissions, committees assess candidates on attributes such as test scores, GPA, and research experience to evaluate their overall fit for the program. Another example arises in medical care where clinicians use patient reports of symptoms to consider preliminary diagnoses and assess risks. Each setting involves mapping multiple criteria to an overall evaluation -- a process that reflects the evaluator's underlying preferences. We focus on the fundamental question of learning these preferences. Many applications of this problem make specific modeling assumptions on evaluator preferences that may be substantially violated in the real world. We make the minimal assumption that the preference function is coordinate-wise non-decreasing, which is reasonable in a large number of evaluation settings. We theoretically characterize the severity of model mismatch for many common assumptions and show that it can lead to significant issues for learning evaluator preferences and other important downstream tasks. We then present an algorithm for learning evaluators' preferences that is robust to model mismatch. We prove theoretically that our algorithm can learn any preference function without sacrificing performance when the linearity assumption holds. Evaluations of our algorithm with synthetic simulations and real-world data confirm its ability to learn preferences robustly and illustrate key aspects of LLM and human preferences.

Problem

• evaluator preferences
• preference learning
• model mismatch
• multi-criteria evaluation
• non-decreasing preference

Innovation

• preference learning
• model misspecification
• monotonicity
• robust algorithm
• evaluator modeling