2M大小模型定义表格理解极限，清华大学崔鹏团队开源LimiX-2M

Core Insights - The article discusses the limitations of modern deep learning models, particularly large language models (LLMs), in handling structured tabular data, which is prevalent in critical systems like power grid scheduling and user modeling [2][3] - It introduces LimiX, a new model developed by Tsinghua University's Cui Peng team, which outperforms traditional models like XGBoost and CatBoost in various tasks while maintaining a compact size of only 2 million parameters [3][5] Performance Comparison - LimiX-2M ranks second in average performance across 11 authoritative benchmarks, just behind LimiX-16M, showcasing its strong zero-shot capabilities [7] - In classification tasks, LimiX-16M and LimiX-2M secured the top two positions, significantly outperforming industry benchmarks like AutoGluon [9] - LimiX-2M achieved an AUC of 0.858 and an accuracy of 0.787 in the BCCO-CLS benchmark, demonstrating its competitive edge [8] Model Features - LimiX-2M is designed to be lightweight and user-friendly, allowing researchers to focus on scientific problems rather than computational challenges [12] - It supports multiple tasks, including classification, regression, and missing value imputation, making it versatile for cross-disciplinary research [13] - The model employs a Radial Basis Function (RBF) embedding mechanism, enhancing its ability to capture complex data patterns without relying on large parameter counts [16][22] Training and Adaptability - LimiX-2M can be fine-tuned to improve performance, achieving an AUC increase of 11.4% with significantly lower time consumption compared to other models [9][10] - The model's architecture allows it to run efficiently on consumer-grade hardware, making it accessible for smaller research teams [13] Conclusion - LimiX-2M represents a significant advancement in structured data modeling, offering high performance with reduced resource requirements, making it suitable for both research and practical applications [26]