Reproduce “DataSage: Multi-agent Collaboration for Insight Discovery with External Knowledge Retrieval, Multi-role Debating, and Multi-path Reasoning” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Xiaochuan Liu, Yuanfeng Song, Xiaoming Yin, Xing Chen
DataSage, developed by ByteDance, introduces a multi-agent framework for automated data insight discovery, integrating external knowledge retrieval, multi-role debating, and multi-path reasoning. It achieves an average improvement of +7.5% on insight-level and +13.9% on summary-level G-Eval scores compared to the best baseline on the InsightBench dataset.
Key insights
- Dynamic retrieval and integration of external knowledge significantly enrich analytical context, leading to more accurate and actionable insights.
- Simulating diverse analytical roles through a debating mechanism enables the generation of deeper, multi-faceted analytical questions.
- Combining multi-path reasoning for code generation with subsequent rigorous refinement improves code correctness and the reliability of derived insights and visualizations.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.