建立标准化评估框架，对比不同表格编码器的表示能力。
TRL-Bench: Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders
- 按行、列、表粒度统一输出嵌入，支持跨范式比较。
- 20个模型在16个任务中表现各异，无单一最优编码器。
- 适合研究表格表示学习与下游任务适配性的学者。
表格编码器通常在特定任务端到端流程中评估，导致不同训练范式模型难以直接比较。本文提出TRL-Bench，一个多层次表格表示学习基准，标准化跨范式表示级评估：每个编码器通过支持的包装器导出行、列或表嵌入，共享轻量头在三个套件中探测：TRL-CTbench（列/表）、TRL-Rbench（行）、TRL-DLTE（涵盖三类粒度的复合数据湖表增强）。为支持该标准设置，我们发布整理的基准资源与任务重构，包括50个OpenML表格及123个验证目标、16个行对链接重写任务，以及从1,379个父表构建的47,772张表的DLTE数据湖。在20个模型和16个任务上，TRL-Bench显示：一旦下游条件标准化，编码器质量取决于具体能力而非单一排行榜。在TRL-CTbench中，通用文本编码器在依赖表面文本信号的任务上表现更优，而表格专精编码器在预训练目标与任务对齐时胜出。在TRL-Rbench中，表内预测与跨表链接偏好不同训练策略，原子链接性能与DLTE流水线中的行匹配阶段强相关。在TRL-DLTE中，最强方案结合能力匹配的专精模型，而非复用单一编码器，且端到端性能取决于非加性组合适配性，而非各阶段边际排名之和。TRL-Bench为在共享下游条件下测量可复用信号提供统一协议。
原文摘要 · Abstract (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
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