构建标准化框架，系统评估大模型自动表格特征工程的效率与效果。
LATTEArena: An Evaluation Framework for LLM-powered Tabular Feature Engineering (Extended Version)
- 将15种特征工程方法归为6维分类，拆解为可复用模块。
- 在7个研究问题上测试24种配置，发现成本与性能的17项权衡关系。
- 提供实操建议，适合想落地LLM特征工程的研究者和开发者。
特征工程是表格数据分析的核心，大语言模型（LLMs）为自动化特征工程提供了新范式，催生了LLM驱动的自动表格特征工程（LATTE）。然而，该领域缺乏标准化、成本感知的评估平台，且设计选择组合爆炸，掩盖了真正的算法进展。为此，我们系统地将15种代表性LATTE方法分解为统一的6维分类体系。基于此抽象，提出LATTEArena——一个标准化、模块化、可扩展的基准测试框架，将复杂的流水线解耦为可重用的执行单元。通过压缩庞大的组合空间，我们在7个研究问题上评估了24种核心LATTE配置。头对头对比不仅关注预测精度，还量化了令牌效率与执行鲁棒性，揭示了17项关于成本效益权衡的实证发现，并提出3条实际部署建议。通过支持可控的组件级比较，LATTEArena推动评估范式从随意提示工程转向系统性上下文管理。所有代码、数据集及超过4000条执行日志均公开可用，以促进社区共建。框架、排行榜及全部资源已发布于LATTEArena项目官网：https://goodenhak.github.io/LATTEArena。
原文摘要 · Abstract (English)
Feature engineering remains a cornerstone of tabular data analysis, and Large Language Models (LLMs) have emerged as a promising paradigm for its automation, giving rise to LLM-powered Automated Tabular Feature Engineering (LATTE). However, the field lacks standardized, cost-aware evaluation platforms, and the combinatorial explosion of design choices obscures true algorithmic progress. To bridge these gaps, we systematically deconstruct 15 representative LATTE methods into a unified 6-dimensional taxonomy. Based on this abstraction, we introduce LATTEArena, a standardized, modular, and extensible benchmarking framework that decouples monolithic pipelines into reusable execution blocks. By distilling the massive combinatorial space, we evaluate 24 core LATTE configurations across 7 research questions. Our head-to-head benchmarking goes beyond predictive accuracy to quantify token efficiency and execution robustness, yielding 17 empirical findings on cost-effectiveness trade-offs. Furthermore, we provide 3 concrete recommendations for optimal real-world deployment. By enabling controlled component-level comparisons, LATTEArena shifts the paradigm from ad-hoc prompt engineering to systematic context management. All code, datasets, and over 4,000 execution logs are publicly available to foster a dynamic, community-driven benchmark. Our framework, leaderboard, and all artifacts are hosted on the LATTEArena project website at https://goodenhak.github.io/LATTEArena.
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