Newest papers

When Do LLMs Help with Feature Selection? A Systematic Empirical Study

Fateme Memar, Dongjie Wang, Arvin Agah · 2026 · arXiv

DOI: 10.48550/arXiv.2610.28556 · open-access PDF

Chinese

大型语言模型(LLMs)可以利用语义特征名称以及表格特征选择的汇总统计数据,但尚不清楚此类信息何时能改善与已建立的统计方法相比的子组选择。我们在一个18个数据集框架内调查这个问题,其中包括10个集群相对政策优化(GRPO)培训的数据集,2个检查点验证的数据集和6个持久的评估数据集。我们比较了五个经典的基线,冷冻的Qwen2.5-7B和Llama-3.3-70B选择器,以及两个GRPO适应的Qwen2.5-7B模型,报告分类ROC-AU

Machine translation (Meta M2M100); the English original follows.

Abstract

Large language models (LLMs) can leverage semantic feature names alongside summary statistics for tabular feature selection, yet it remains unclear when such information improves subset selection relative to established statistical methods. We investigate this question within an 18-dataset framework comprising 10 datasets for Group Relative Policy Optimization (GRPO) training, 2 for checkpoint validation, and 6 held-out evaluation datasets. We compare five classical baselines, frozen Qwen2.5-7B and Llama-3.3-70B selectors, and two GRPO-adapted Qwen2.5-7B models, reporting classification ROC-AU

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