Table-specific pretraining of Llama-2 yields significant gains on zero-shot, few-shot, and in-context tabular prediction tasks over prior benchmarks.
Pasta: table-operations aware fact verification via sentence-table cloze pre-training
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
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
-
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.
-
When TableQA Meets Noise: A Dual Denoising Framework for Complex Questions and Large-scale Tables
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.