用大模型生成代码，整合多个AutoML工具，高效处理表格数据任务
LightAutoDS-Tab: Multi-AutoML Agentic System for Tabular Data
- 大模型生成代码，动态调用多个AutoML工具构建流水线
- 在Kaggle多个任务上超越当前开源最优方案，提升效率与鲁棒性
- 适合需要快速构建表格数据流水线的研究者和工程师
AutoML在结合大语言模型（LLM）处理复杂任务方面取得进展，但仍受限于对特定底层工具的依赖。本文提出LightAutoDS-Tab，一种面向表格数据的多AutoML智能体系统，将基于大模型的代码生成与多种AutoML工具融合。该方法提升了流水线设计的灵活性与鲁棒性，在Kaggle多个数据科学任务中表现优于现有开源最优方案。代码已开源，地址为https://github.com/sb-ai-lab/LADS。
原文摘要 · Abstract (English)
AutoML has advanced in handling complex tasks using the integration of LLMs, yet its efficiency remains limited by dependence on specific underlying tools. In this paper, we introduce LightAutoDS-Tab, a multi-AutoML agentic system for tasks with tabular data, which combines an LLM-based code generation with several AutoML tools. Our approach improves the flexibility and robustness of pipeline design, outperforming state-of-the-art open-source solutions on several data science tasks from Kaggle. The code of LightAutoDS-Tab is available in the open repository https://github.com/sb-ai-lab/LADS
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。