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arXiv · 2507.00828

ProxAnn: Use-Oriented Evaluations of Topic Models and Document Clustering

Alexander Hoyle·Lorena Calvo-Bartolomé·Jordan Boyd-Graber·Philip Resnik
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Abstract

Topic model and document-clustering evaluations either use automated metrics that align poorly with human preferences or require expert labels that are intractable to scale. We design a scalable human evaluation protocol and a corresponding automated approximation that reflect practitioners' real-world usage of models. Annotators -- or an LLM-based proxy -- review text items assigned to a topic or cluster, infer a category for the group, then apply that category to other documents. Using this protocol, we collect extensive crowdworker annotations of outputs from a diverse set of topic models on two datasets. We then use these annotations to validate automated proxies, finding that the best LLM proxies are statistically indistinguishable from a human annotator and can therefore serve as a reasonable substitute in automated evaluations. Package, web interface, and data are at https://github.com/ahoho/proxann

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Alexander Hoyle, Lorena Calvo-Bartolomé, Jordan Boyd-Graber, Philip Resnik. 2025-07-01. ProxAnn: Use-Oriented Evaluations of Topic Models and Document Clustering. https://arxiv.org/abs/2507.00828

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