Corpus-Centric Learning for Zero-Shot Table Retrieval

Abstract

Tabular data represents a major source of structured knowledge for open-domain question answering (QA) and enterprise data lakes, yet effective table retrieval remains challenging due to the structure–semantics gap imposed by tabular layouts. Heuristic table linearization often leads to semantic loss, particularly for implicit queries, while recent supervised retrieval models (e.g., Birdie, Contr) rely heavily on large-scale query logs and labeled QA pairs, limiting their applicability in Day-0 cold-start scenarios. We propose GeCo-TR (Generative Schema and Contrastive Table Retrieval), a zero-shot table retrieval framework that eliminates the need for supervised QA data by shifting from direct query-to-table learning to modeling the intrinsic structural semantics of the table corpus. GeCo-TR introduces UHMI, a unified hybrid representation that integrates table structure with linked knowledge graph entities, and employs a hybrid neural–symbolic retrieval mechanism that dynamically combines dense semantic retrieval, symbolic graph traversal, and sparse lexical matching. This design enables robust semantic generalization while enforcing explicit structural constraints, resulting in high-precision and high-recall retrieval for implicit queries in a zero-shot setting. Extensive experiments on public benchmarks demonstrate the effectiveness of GeCo-TR; notably, on Open-WikiTable, it achieves 97.5% Recall@5 in the zero-shot setting, ranking second among all evaluated methods despite requiring no query-level supervision.