PIPER retrieves and ranks tabular datasets by profiling their content and using LLM-generated queries for dense vector search, outperforming metadata baselines and TableQA methods in low-metadata settings.
TaBERT: Pretraining for joint understanding of textual and tabu- lar data
8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
verdicts
UNVERDICTED 8representative citing papers
STC reduces tabular chunk counts by up to 56% versus baselines and raises hybrid MRR to 0.5945 and BM25 Recall@1 to 0.754 by preserving row structure during chunking.
KaSLA applies knapsack optimization hierarchically to schema linking for LLM text-to-SQL, claiming better results than large models and improved SQL generation on Spider and BIRD.
TaNOS decouples table semantics from numerical structure via anonymization, sketches, and program-first self-supervision, yielding 80.13% FinQA accuracy with 10% data and near-zero cross-domain gap versus over 10pp for standard fine-tuning.
TabEmb decouples LLM-based semantic column embeddings from graph-based structural modeling to produce joint representations that improve table annotation tasks.
XiYan-SQL achieves SOTA Text-to-SQL accuracy by combining schema filtering, a multi-generator ensemble fine-tuned on varied SQL formats, and a selection model.
Table-specific pretraining of Llama-2 yields significant gains on zero-shot, few-shot, and in-context tabular prediction tasks over prior benchmarks.
EnoTab is a dual denoising framework for TableQA that performs evidence-based question denoising via semantic unit decomposition and evidence tree-guided table pruning with post-order rollback to improve performance on complex questions and large-scale tables.
citing papers explorer
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Unlock the Potential of Large Language Models for Predictive Tabular Tasks in Data Science with Table-Specific Pretraining
Table-specific pretraining of Llama-2 yields significant gains on zero-shot, few-shot, and in-context tabular prediction tasks over prior benchmarks.