One Table Benchmark 推出：通用表格模型在生成、插补和预测任务中的基准测试
One Table, Many Queries: Benchmarking Generalist Tabular Models Across Generation, Imputation, and Prediction
One Table Benchmark 测试了 46 种模型在 56 个数据集上的表格生成、插补和预测能力，发现这些任务共享条件信息但保真度和目标交互不同，固定模型在效用和保真度间存在权衡，冻结的基础模型在预测和数值插补方面表现突出，表明统一表格模型有潜力提升分类重建和联合采样。
Published on Nov 8
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Abstract
A large aligned benchmark reveals that tabular generation, imputation, and prediction share useful conditional information but show distinct fidelity rankings and objective interactions, motivating unified models that improve categorical reconstruction and joint sampling.
Generation, imputation, and prediction query different aspects of the same tabular distribution, but are usually evaluated separately. This leaves unclear which capabilities they share, how their training objectives interact, and whether one model can perform all three well. We introduce the One Table Benchmark (), spanning 56 datasets, 46 registered methods, and 20 capability axes under aligned data splits and explicit task contracts. We compare task ranks on aligned support, train five backbones under all seven non-empty objective combinations on five datasets with three seeds, and contrast fixed-model profiles with specialist portfolios. Rank comparisons connect imputation and prediction through numerical recovery and dependence preservation, and link generation to both through predictive utility; distributional fidelity follows different, sometimes opposing, rankings. Objective interventions show the largest pooled gains from including the recipient's own objective. Generation has the broadest coverage of positive pooled auxiliary effects, while prediction shows potential to improve imputation and synthetic-data utility, with responses that vary across backbones. Cross-addition effects reveal that objective benefits need not add and depend on the objectives already present. Fixed-model comparisons expose corresponding utility--fidelity trade-offs: some generalists surpass specialist portfolios on utility, while fidelity continues to favor specialization. Frozen foundation models show strong prediction and numerical point imputation, together with promising synthetic-data utility, highlighting their potential across all three tasks. These findings motivate unified tabular models that share useful conditional information while improving categorical reconstruction and faithful joint sampling.
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来源: Hugging Face Daily Papers · huggingface.co