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Data Interpreter: An LLM Agent For Data Science (2402.18679v4)

Published 28 Feb 2024 in cs.AI and cs.LG

Abstract: LLM-based agents have shown effectiveness across many applications. However, their use in data science scenarios requiring solving long-term interconnected tasks, dynamic data adjustments and domain expertise remains challenging. Previous approaches primarily focus on individual tasks, making it difficult to assess the complete data science workflow. Moreover, they struggle to handle real-time changes in intermediate data and fail to adapt dynamically to evolving task dependencies inherent to data science problems. In this paper, we present Data Interpreter, an LLM-based agent designed to automatically solve various data science problems end-to-end. Our Data Interpreter incorporates two key modules: 1) Hierarchical Graph Modeling, which breaks down complex problems into manageable subproblems, enabling dynamic node generation and graph optimization; and 2) Programmable Node Generation, a technique that refines and verifies each subproblem to iteratively improve code generation results and robustness. Extensive experiments consistently demonstrate the superiority of Data Interpreter. On InfiAgent-DABench, it achieves a 25% performance boost, raising accuracy from 75.9% to 94.9%. For machine learning and open-ended tasks, it improves performance from 88% to 95%, and from 60% to 97%, respectively. Moreover, on the MATH dataset, Data Interpreter achieves remarkable performance with a 26% improvement compared to state-of-the-art baselines. The code is available at https://github.com/geekan/MetaGPT.

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Citations (31)
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Summary

  • The paper presents Data Interpreter, which uses dynamic planning with hierarchical graphs to adapt to evolving data challenges.
  • The paper enhances coding proficiency by dynamically integrating specialized tools, reducing errors and boosting performance metrics significantly.
  • The paper validates its approach with notable improvements across benchmarks, setting a new standard for LLM application in data science.

Enhancing LLMs for Data Science: Introducing the Data Interpreter

Introduction to Data Interpreter

In the evolving landscape of LLMs used as agents across various domains, there remains a notable gap in adapting these models to tackle the intrinsic complexities of data science tasks. The Data Interpreter emerges as a notable advancement, addressing the challenges inherent in data science scenarios—comprising real-time data adjustments, intricate dependencies amongst varied tasks, and the essential identification of logical inconsistencies for precise reasoning. This solution is underpinned by three core techniques: dynamic planning with hierarchical graph structures, dynamic integration of tools to augment coding proficiency, and the enhancement of reasoning through logical inconsistency identification and experience recording.

Addressing Data Science Challenges

The overarching challenges in adapting LLMs for data science tasks revolve around several key points:

  • Dynamic Data Adaptability: The necessity for real-time adjustment to evolving data and variable dependencies, especially prevalent in machine learning modeling processes.
  • Domain-Specific Expertise: The requirement for refined domain knowledge imbedded within code solutions, addressing the gap in existing LLM capabilities that lack direct access to such specialized insight.
  • Logical Consistency Requirement: An essential aspect wherein LLMs must not only execute code error-free but also verify the logical soundness of solutions despite ambiguous and irregular requirements characterizing data science problems.

Core Innovations of Data Interpreter

Dynamic Planning and Hierarchical Structure: This approach allows for an adaptable framework capable of managing the dynamic nature of data science tasks, effectively tracking data changes and variable dependencies through a well-structured hierarchical graph model.

Tool Utilization and Generation: By dynamically integrating and generating tools, the Data Interpreter significantly enhances coding proficiency, moving beyond basic API calls to employing a variety of tools tailored for specific tasks, thereby facilitating more efficient and accurate code solutions.

Enhanced Reasoning with Logical Bug Awareness: Utilizing confidence scores derived from execution results and test-driven validations, this technique offers a novel method for detecting inconsistencies between code solutions and expected outcomes, substantially reducing logical errors and improving the solution's reliability.

Experimental Validation and Results

The Data Interpreter's superior performance is evidenced through its evaluation across different benchmarks, including machine learning tasks, the MATH dataset, and real-world scenarios. Significant improvements were seen in machine learning tasks (improvement from 0.86 to 0.95), a 26% increase in the MATH dataset, and a remarkable 112% improvement in open-ended tasks. These results not only showcase the model's robust problem-solving capabilities but also set a new standard for LLM performance in data science applications.

Implications and Future Directions

The development and implementation of the Data Interpreter represent a significant step forward in the deployment of LLMs within the domain of data science. By addressing critical gaps and introducing innovative solutions for dynamic data adaptability, tool integration, and logical inconsistency identification, this model paves the way for more efficient, accurate, and reliable data science workflows. The framework's ability to dynamically adjust to real-time data changes, coupled with its advancement in tool utilization and logical reasoning, opens up new avenues for research and application in AI-driven data analysis. Future developments may focus on further enhancing the model's adaptability and reasoning capabilities, potentially incorporating more sophisticated mechanisms for tool generation and logical validation, thereby broadening the scope of LLM applications in tackling the complexities of data science.

In conclusion, the Data Interpreter marks a definitive advance in the application of LLMs to data science, offering a pragmatic and effective solution to previously unmet challenges. Its success heralds a promising direction for future research in the intersection of AI and data science, aiming to unlock new potentials and drive further innovation in this pivotal field.

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