title: LAMBDA: A Large Model Based Data Agent
summary: 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.
paper_authors._id: 66a31decc1886a250bbda980
paper_authors.name: Maojun Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-26T07:48:55.476Z
paper_authors._id: 64c0e071e9263c783d548178
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64c0e071e9263c783d548178/VFwxjv7HFPwIoKIVGWLDg.png
paper_authors.fullname: Maojun SUN
paper_authors.type: user
paper_authors.user: Stephen-smj
paper_authors._id: 66a31decc1886a250bbda981
paper_authors.name: Ruijian Han
paper_authors._id: 66a31decc1886a250bbda982
paper_authors.name: Binyan Jiang
paper_authors._id: 66a31decc1886a250bbda983
paper_authors.name: Houduo Qi
paper_authors._id: 66a31decc1886a250bbda984
paper_authors.name: Defeng Sun
paper_authors._id: 66a31decc1886a250bbda985
paper_authors.name: Yancheng Yuan
paper_authors._id: 66a31decc1886a250bbda986
paper_authors.name: Jian Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-30T07:26:20.764Z
paper_authors._id: 66a7bd887747a5de8e90970b
paper_authors.avatarUrl: /avatars/10ee13b29364af960158aba023dec37b.svg
paper_authors.fullname: Jian Huang
paper_authors.type: user
paper_authors.user: jianlambda
paper_title: LAMBDA: A Large Model Based Data Agent
paper_summary: 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.
paper_ai_summary: LAMBDA is an open-source, code-free multi-agent system that uses advanced models and human intervention to perform iterative and generative data analysis through natural language.