LAMBDA：基於大型模型的資料代理程式

LAMBDA: A Large Model Based Data Agent

July 24, 2024

作者: Maojun Sun, Ruijian Han, Binyan Jiang, Houduo Qi, Defeng Sun, Yancheng Yuan, Jian Huang

cs.AI

摘要

我們介紹了一個新穎的開源、無需編碼的多智能體資料分析系統「LAMBDA」，利用大型模型的強大功能。LAMBDA旨在通過創新設計的資料智能體，以自然語言迭代並生成，解決複雜數據驅動應用中的資料分析挑戰。LAMBDA的核心是兩個關鍵智能體角色：程式設計師和檢查員，它們被設計成能夠無縫協作。具體而言，程式設計師根據用戶的指令和領域特定知識生成代碼，並借助先進模型進行增強。與此同時，檢查員在必要時對代碼進行調試。為確保穩健性並應對不利情況，LAMBDA提供用戶界面，允許用戶直接介入運行循環。此外，LAMBDA可以通過我們的知識整合機制靈活集成外部模型和算法，滿足定制化資料分析的需求。LAMBDA在各種機器學習數據集上展現出優異性能。它有潛力通過無縫整合人工智能和人類智慧，使來自不同背景的個人更容易、更有效率地進行資料科學實踐和分析範式。LAMBDA在解決資料科學問題方面的優異表現在多個案例研究中得到展示，請參見https://www.polyu.edu.hk/ama/cmfai/lambda.html。

English

We introduce ``LAMBDA," a novel open-source, code-free multi-agent data analysis system that that harnesses the power of large models. LAMBDA is designed to address data analysis challenges in complex data-driven applications through the use of innovatively designed data agents that operate iteratively and generatively using natural language. At the core of LAMBDA are two key agent roles: the programmer and the inspector, which are engineered to work together seamlessly. Specifically, the programmer generates code based on the user's instructions and domain-specific knowledge, enhanced by advanced models. Meanwhile, the inspector debugs the code when necessary. To ensure robustness and handle adverse scenarios, LAMBDA features a user interface that allows direct user intervention in the operational loop. Additionally, LAMBDA can flexibly integrate external models and algorithms through our knowledge integration mechanism, catering to the needs of customized data analysis. LAMBDA has demonstrated strong performance on various machine learning datasets. It has the potential to enhance data science practice and analysis paradigm by seamlessly integrating human and artificial intelligence, making it more accessible, effective, and efficient for individuals from diverse backgrounds. The strong performance of LAMBDA in solving data science problems is demonstrated in several case studies, which are presented at https://www.polyu.edu.hk/ama/cmfai/lambda.html.