LAMBDA: A Large Model Based Data Agent
Authors: name: Maojun Sun, name: Ruijian Han, name: Binyan Jiang, name: Houduo Qi, name: Defeng Sun, name: Yancheng Yuan, name: Jian Huang

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.