AI表格模型评测：没有万能冠军，只有专才
AI处理表格数据（如Excel、数据库）时，不同模型各有所长，但过去评测方式混乱，无法直接比较。这篇论文推出了TRL-Bench，一个标准化评测框架，把表格编码器的能力拆成行、列、表三个粒度，用统一方式测试。结果发现：没有模型在所有任务上称王——通用文本编码器在依赖文字描述的任务上领先，而专为表格设计的模型在与其预训练目标匹配的任务上胜出。更关键的是，在复杂的数据湖表增强任务中，最佳方案是组合多个专才模型，而非依赖单一全能模型。这告诉你：别迷信“最强AI表格模型”，选对工具比选“最好”的工具更重要。
📄 原文摘要(英文)
Tabular encoders are usually evaluated inside task-specific end-to-end pipelines, so models from different training paradigms are difficult to compare directly even when they operate on similar tabular signals. We introduce TRL-Bench, a multi-granular tabular representation learning (TRL) benchmark that standardizes cross-paradigm representation-level evaluation: each encoder exports row-, column-, or table embeddings through its supported wrapper, and shared lightweight heads probe them across three suites: TRL-CTbench (column/table), TRL-Rbench (row), and TRL-DLTE (compositional Data-Lake Table Enrichment spanning all three granularities). To support this standardized setting, we release curated benchmark assets and task reformulations, including 50 OpenML tables with 123 verified targets, 16 row-pair linkage rewrites, and a 47,772-table DLTE lake derived from 1,379 parent tables. Across 20 models and 16 tasks, TRL-Bench shows that once downstream conditions are standardized, encoder quality is capability-specific rather than captured by a single leaderboard. In TRL-CTbench, generic text encoders often lead on tasks with strong surface-text signal, while tabular specialists win where their pretraining objective aligns with the task. In TRL-Rbench, within-table prediction and cross-table linkage favor different training regimes, with atomic linkage performance correlating strongly with the row-matching stage of DLTE pipelines. In TRL-DLTE, the strongest pipelines combine capability-matched specialists rather than reuse a single encoder, and top end-to-end quality depends on non-additive compositional fit rather than per-stage marginal rank alone. TRL-Bench provides a common protocol for measuring reusable signal in exported tabular representations under shared downstream conditions. Code and data: https://github.com/LOGO-CUHKSZ/TRL-Bench