Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering

Abstract:Natural language conveys information at varying levels of granularity, from fine-grained references to broad descriptions. While granularity is fundamental to human communication, existing measures mostly capture surface detail or sentence specificity. We introduce Granuscore, a reference-free measure of granularity that leverages structural properties of a hierarchical embedding space. Granuscore reliably recovers hierarchical orderings on the Granola-EQ dataset and captures expected differences in granularity across discourse contexts. Across domains, we further show that Granuscore explains non-linear variation in sentence specificity beyond sentence length. Finally, we apply Granuscore to four question-answering benchmarks and analyze how granularity differs for questions, gold answers, and model outputs across response outcomes. The analysis reveals consistent differences in model behavior and provides a principled lens for characterizing the difficulty of QA datasets. Together, the results position Granuscore as a scalable, broadly applicable tool for analyzing granularity in text.

Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)

arXiv:2605.26620 [cs.CL]

(or arXiv:2605.26620v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.26620 Focus to learn more arXiv-issued DOI via DataCite

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