TRL-Bench：标准化跨范式表示级别的表格编码器评估

摘要

表格编码器通常在特定任务的端到端流水线中进行评估，因此即使不同训练范式的模型处理相似的表格信号，也难以直接比较。我们提出TRL-Bench，一个多粒度表格表示学习（TRL）基准，用于标准化跨范式的表示级评估：每个编码器通过其支持的封装器导出行嵌入、列嵌入或表嵌入，共享轻量级探针在三个套件中对其进行探测：TRL-CTbench（列/表）、TRL-Rbench（行）和TRL-DLTE（涵盖所有三种粒度的组合式数据湖表增强）。为支持这一标准化设置，我们发布了精选的基准资产和任务重构，包括50个OpenML表格（含123个已验证目标）、16个行对链接重写任务，以及一个由1,379个父表衍生出的47,772表DLTE数据湖。在20个模型和16个任务上的实验表明，一旦下游条件标准化，编码器质量具有能力特异性，而非由单一排行榜决定。在TRL-CTbench中，通用文本编码器在表面文本信号较强的任务上通常领先，而表格专用模型在其预训练目标与任务对齐时胜出。在TRL-Rbench中，表内预测和跨表链接偏好不同的训练体制，其中原子链接性能与DLTE流水线的行匹配阶段高度相关。在TRL-DLTE中，最强流水线结合了能力匹配的专用模型，而非重复使用单一编码器，且顶级端到端质量取决于非加性的组合适配度，而非各阶段的边际排名。TRL-Bench为在共享下游条件下测量导出表格表示中的可复用信号提供了通用协议。代码和数据：https://github.com/LOGO-CUHKSZ/TRL-Bench

English

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