LDI introduces localized LLM-based imputation for text-rich tables by selecting compact relevant subsets of attributes and tuples per missing value, reporting up to 8% accuracy gains over prior methods.
arXiv preprint arXiv:2210.06710 , year=
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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.
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LDI: Localized Data Imputation for Text-Rich Tables
LDI introduces localized LLM-based imputation for text-rich tables by selecting compact relevant subsets of attributes and tuples per missing value, reporting up to 8% accuracy gains over prior methods.
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Generalizing Numerical Reasoning in Table Data through Operation Sketches and Self-Supervised Learning
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.