LLMTabBench evaluates LLMs on zero- and few-shot binary tabular classification and reports that zero-shot can outperform few-shot due to example conflicts with model priors while performance drops beyond a complexity threshold.
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ReSS extracts decision paths from trees as scaffolds to guide LLM reasoning generation, fine-tunes the LLM on the resulting dataset with scaffold-invariant augmentation, and reports up to 10% gains on medical and financial tabular benchmarks with new faithfulness metrics.
TAROT constructs and refines LLM-derived task-adaptive semantic graphs then applies GNN message passing to improve few-shot tabular prediction.
Table-specific pretraining of Llama-2 yields significant gains on zero-shot, few-shot, and in-context tabular prediction tasks over prior benchmarks.
citing papers explorer
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LLMTabBench: Evaluating LLMs on Binary Tabular Classification From Zero to Few Shots
LLMTabBench evaluates LLMs on zero- and few-shot binary tabular classification and reports that zero-shot can outperform few-shot due to example conflicts with model priors while performance drops beyond a complexity threshold.
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ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold
ReSS extracts decision paths from trees as scaffolds to guide LLM reasoning generation, fine-tunes the LLM on the resulting dataset with scaffold-invariant augmentation, and reports up to 10% gains on medical and financial tabular benchmarks with new faithfulness metrics.
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TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning
TAROT constructs and refines LLM-derived task-adaptive semantic graphs then applies GNN message passing to improve few-shot tabular prediction.
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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.