为表格数据中的文本特征设计新评测基准，推动模型能力评估
Towards Benchmarking Foundation Models for Tabular Data With Text
- 提出简单有效的文本融合策略，适配传统表格处理流程
- 人工筛选真实世界含语义文本的表格数据集用于评测
- 填补现有基准对文本特征支持的空白，适合研究表格多模态模型者
表格数据的基础模型快速发展，越来越多研究尝试扩展其支持自由文本特征。然而，现有表格数据基准极少包含文本列，且在真实场景中寻找富含语义的文本特征表格数据颇具挑战。本文提出一系列简单而有效的消融式策略，将文本信息融入传统表格处理流程。同时，通过手动整理一批具有实际意义文本特征的真实世界表格数据集，评测当前最先进的表格基础模型在处理文本数据时的表现。本研究是提升表格数据基础模型文本能力评测的重要一步。
原文摘要 · Abstract (English)
Foundation models for tabular data are rapidly evolving, with increasing interest in extending them to support additional modalities such as free-text features. However, existing benchmarks for tabular data rarely include textual columns, and identifying real-world tabular datasets with semantically rich text features is non-trivial. We propose a series of simple yet effective ablation-style strategies for incorporating text into conventional tabular pipelines. Moreover, we benchmark how state-of-the-art tabular foundation models can handle textual data by manually curating a collection of real-world tabular datasets with meaningful textual features. Our study is an important step towards improving benchmarking of foundation models for tabular data with text.
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