Paper page - Diversed Model Discovery via Structured Table Discovery

Source: https://huggingface.co/papers/2605.22766

Abstract

Model search system that combines semantic and structured table-based retrieval to improve diversity and coverage of recommended models.

Model cards describe model behavior through a mixture of textual descriptions and structured artifacts, including performance, configuration, and dataset tables. Existingmodel searchsystems rely predominantly onsemantic similarityover text, which can produce homogeneous result sets and limit exploration of alternatives. We argue thatmodel searchis inherently comparative: users want models that are task-aligned yet differentiated in measurable ways. We hypothesize that this balance requires retrieval over condensed, high-quality evidence rather than verbose descriptions, and much of that evidence is concentrated instructured tables. We present StructuredSemanticSearch, a table-drivenmodel searchframework built on the ModelTables benchmark. Given a query, StructuredSemanticSearch combines a semantic baseline for task alignment with a structure-aware pipeline that discovers query-related model-card tables usingtable discovery operatorssuch asunionability,joinability, and keyword search. Retrieved tables are mapped back to model cards under a controlled top-k budget, enabling fair comparison between text-based and table-based retrieval. Beyond retrieval, StructuredSemanticSearch adaptstable integrationto the model-table domain throughorientation-aware integration, producing compact integrated views of tables from partially overlapping and sometimes transposed evidence tables. For evaluation, we introduce a nugget-based, auditable protocol that extracts compact evidence items from model cards, matches queries to condition- or intent-specific nuggets, and measuresevidence coverageand diversity over retrieved model-card candidate sets. This protocol also provides a scalable path toward approximate, evidence-based labeling in dynamic model lakes. Experiments on 597 model-recommendation queries show improved nugget coverage for the structure-aware pipeline than semantic baseline

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