Topic-Specific Classifiers are Better Relevance Judges than Prompted LLMs

• URL: http://arxiv.org/abs/2510.04633v1
• Date: Mon, 06 Oct 2025 09:38:13 GMT
• Title: Topic-Specific Classifiers are Better Relevance Judges than Prompted LLMs
• Authors: Lukas Gienapp, Martin Potthast, Harrisen Scells, Eugene Yang,
• Abstract summary: The unjudged document problem is a key obstacle to the reusability of test collections in information retrieval.<n>We train topic-specific relevance classifiers by finetuning monoT5 with independent LoRA weight adaptation on the judgments of a single assessor.<n>As little as 128 initial human judgments per topic suffice to improve the comparability of models.
• Score: 34.14678608130442
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The unjudged document problem, where pooled test collections have incomplete relevance judgments for evaluating new retrieval systems, is a key obstacle to the reusability of test collections in information retrieval. While the de facto standard to deal with the problem is to treat unjudged documents as non-relevant, many alternatives have been proposed, including the use of large language models (LLMs) as a relevance judge (LLM-as-a-judge). However, this has been criticized as circular, since the same LLM can be used as a judge and as a ranker at the same time. We propose to train topic-specific relevance classifiers instead: By finetuning monoT5 with independent LoRA weight adaptation on the judgments of a single assessor for a single topic's pool, we align it to that assessor's notion of relevance for the topic. The system rankings obtained through our classifier's relevance judgments achieve a Spearmans' $\rho$ correlation of $>0.95$ with ground truth system rankings. As little as 128 initial human judgments per topic suffice to improve the comparability of models, compared to treating unjudged documents as non-relevant, while achieving more reliability than existing LLM-as-a-judge approaches. Topic-specific relevance classifiers thus are a lightweight and straightforward way to tackle the unjudged document problem, while maintaining human judgments as the gold standard for retrieval evaluation. Code, models, and data are made openly available.

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