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.
Large language models are few (1)-shot table reasoners
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TaNOS improves cross-domain numerical reasoning over tables by combining header anonymization, operation sketches, and self-supervised pretraining, achieving 80.13% accuracy on FinQA with 10% of training data.
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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 improves cross-domain numerical reasoning over tables by combining header anonymization, operation sketches, and self-supervised pretraining, achieving 80.13% accuracy on FinQA with 10% of training data.