One-Shot Labeling for Automatic Relevance Estimation

TL;DR

This paper introduces One-Shot Labelers that leverage large language models to predict relevance of unjudged documents, significantly improving offline search system evaluations and statistical reliability.

Contribution

It presents novel methods for filling relevance assessment holes using large language models, enhancing evaluation accuracy and confidence in search system comparisons.

Findings

Predictions of 1SL often disagree with human assessments.

1SL labels produce more reliable system rankings.

System ranking correlations exceed 0.86 with full rankings.

Abstract

Dealing with unjudged documents ("holes") in relevance assessments is a perennial problem when evaluating search systems with offline experiments. Holes can reduce the apparent effectiveness of retrieval systems during evaluation and introduce biases in models trained with incomplete data. In this work, we explore whether large language models can help us fill such holes to improve offline evaluations. We examine an extreme, albeit common, evaluation setting wherein only a single known relevant document per query is available for evaluation. We then explore various approaches for predicting the relevance of unjudged documents with respect to a query and the known relevant document, including nearest neighbor, supervised, and prompting techniques. We find that although the predictions of these One-Shot Labelers (1SL) frequently disagree with human assessments, the labels they produce yield a far more reliable ranking of systems than the single labels do alone. Specifically, the strongest approaches can consistently reach system ranking correlations of over 0.86 with the full rankings over a variety of measures. Meanwhile, the approach substantially increases the reliability of t-tests due to filling holes in relevance assessments, giving researchers more confidence in results they find to be significant. Alongside this work, we release an easy-to-use software package to enable the use of 1SL for evaluation of other ad-hoc collections or systems.