Discovery-Driven Integration of Disjoint Tables via Text
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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arXiv:2601.13111v3 Announce Type: replace-cross Abstract: Realistic text-to-SQL workflows often require joining multiple tables. As a result, accurately retrieving the relevant set of tables becomes...
Tables are ubiquitous across diverse domains, yet reasoning over them remains a significant challenge for modern large language models (LLMs). Current approaches typically linearize tables into sequen...
H2Table introduces a hierarchical hypergraph representation for complex tables, enabling a hypergraph encoder to capture semantic relationships between headers and cells. The framework uses learnable query vectors to extract structural embeddings for large language models. Experiments on the HiTab dataset show a 22.88% improvement over state‑of‑the‑art baselines on tables with four levels of nesting.
arXiv:2610.00817v1 Announce Type: cross Abstract: Join discovery aims to identify tables from large data repositories that can augment a query table with complementary information, enabling downstrea...
arXiv:2607. 06482v1 Announce Type: cross Abstract: Current benchmarks for evaluating Large Language Models (LLMs) in data analysis often fail to reflect real-world settings.
PARTAB is a framework that improves large language model reasoning on tables by constructing a structured evidence interface. It represents query‑relevant evidence as semantically coherent, row‑linked table regions and performs hierarchical selection over column groups and row‑level partitions before composing the evidence for answer generation. Evaluations on multiple table reasoning benchmarks show that PARTAB consistently outperforms full‑table prompting and recent methods, achieving strong performance on WikiTableQuestions and TabFact while remaining competitive on numerical reasoning tasks.