arXiv AI
CORE-T: COherent REtrieval of Tables for Text-to-SQL
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arXiv AI
Oct 2
JoinGR: Learning to Traverse Join Graphs for Table Retrieval
JoinGR is a join‑aware table retrieval method that treats a database’s join graph as the retrieval space. It represents columns as graph nodes and both intra‑table and foreign‑key relationships as typed edges, then selects anchor tables, traverses join edges with a query‑conditioned scorer, and aggregates edge deposits into table scores. Experiments on BIRD, Spider, and BEAVER show that JoinGR matches or surpasses strong retrieval baselines, especially improving recall on multi‑hop table requirements, and its learned scorer transfers across benchmarks, indicating reusable join‑graph traversal behavior.
arXiv AI
Sep 2
Replacing Training with Memory: Listwise Selection for Text-to-SQL
The paper proposes a fine‑tuning‑free listwise selector for Text‑to‑SQL systems that replaces traditional learning objectives with inference‑time strategies. It introduces reusable structured memories (MaP‑SQL) that encode mappings from natural language to schema elements, SQL operations, and expected outputs, and uses these memories to evaluate candidate queries. To reduce positional bias, the method aggregates rankings across multiple input permutations, optimizing inference cost through execution results and pointwise scoring. The approach achieves higher selection accuracy, fewer unnecessary comparisons, and outperforms the prior state‑of‑the‑art R^3‑SQL on the BIRD‑dev benchmark while using fewer tokens.
arXiv AI
Jun 30
SemJoin: Semantic Join Optimization
arXiv:2606. 29532v1 Announce Type: cross Abstract: Integrating unstructured data into relational database systems is increasingly important as demand grows for natural language querying and analysis.
arXiv Machine Learning
Oct 9
Compile the Table: Query-Calibrated Operator Compression for Tabular In-Context Learning
The paper introduces QCOC, a method for compressing the key‑value cache used in tabular in‑context learning. QCOC clusters in‑context example states into joint‑KV prototypes, calibrates them against query vectors, and preserves multiplicities to maintain accuracy while achieving high compression. Experiments on 64 OpenML‑CC18 datasets and seven long tables show QCOC outperforms other compression and retrieval methods, achieving up to a 10.5× compression ratio and 508× speed‑up over dynamic retrieval baselines while staying within 0.23 percentage points of full‑context accuracy.
arXiv Machine Learning
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MetaJoin: Adaptive Join Selections for Heterogeneous Tables
MetaJoin is a label‑free framework that adaptively selects join methods for heterogeneous tables by first choosing an initial specialist based on input evidence and then validating or repairing the result using specialist‑specific output evidence. It introduces MNN Join, a transformation specialist that finds string transformations by maximizing mutual‑nearest‑neighbor coverage and uses reciprocal validation. Evaluated on 670 datasets, MetaJoin achieves a macro row‑F1 of 0.817, surpassing a benchmark oracle and outperforming other label‑free policies by over 0.13, while MNN Join dominates all transformation baselines on 609 transformation‑heavy datasets.
arXiv Machine Learning
Sep 23
Discovery-Driven Integration of Disjoint Tables via Text
arXiv:2609.26658v1 Announce Type: cross Abstract: Integrating heterogeneous datasets within data lakes is a critical challenge, particularly for semantically related tables that lack the explicit att...