DS-STAR能跨格式处理数据并生成高质量研究报告。
DS-STAR: Data Science Agent for Solving Diverse Tasks across Heterogeneous Formats and Open-Ended Queries
- 跨异构数据源整合信息，支持多文件协作分析
- 在4个基准上达最优，硬任务下表现显著优于基线
- 适合需要复杂数据推理与开放性报告的科研人员
尽管大语言模型在自动化数据科学方面展现出潜力，现有智能体仍难以应对需探索多个数据源并生成开放式洞察的真实世界工作流。本文提出DS-STAR，一种专用智能体，能够（1）无缝处理并整合多种异构格式的数据，（2）超越简单问答，生成完整的数据科学研究报告。大量实验表明，DS-STAR在四个基准测试（DABStep、DABStep-Research、KramaBench、DA-Code）上达到领先性能。尤其在需多文件处理的高难度问答任务中，其表现显著优于现有基线模型；所生成的报告在超过88%的案例中被评价为优于最佳基线模型。
原文摘要 · Abstract (English)
While large language models (LLMs) have shown promise in automating data science, existing agents often struggle with the complexity of real-world workflows that require exploring multiple sources and synthesizing open-ended insights. In this paper, we introduce DS-STAR, a specialized agent to bridge this gap. Unlike prior approaches, DS-STAR is designed to (1) seamlessly process and integrate data across diverse, heterogeneous formats, and (2) move beyond simple QA to generate comprehensive research reports for open-ended queries. Extensive evaluation shows that DS-STAR achieves state-of-the-art performance on four benchmarks: DABStep, DABStep-Research, KramaBench, and DA-Code. Most notably, it significantly outperforms existing baseline models especially in hard-level QA tasks requiring multi-file processing, and generates high-quality data science reports that are preferred over the best baseline model in over 88% of cases.
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