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Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow (2306.07209v7)

Published 12 Jun 2023 in cs.CL, cs.AI, and cs.CE

Abstract: Industries such as finance, meteorology, and energy generate vast amounts of data daily. Efficiently managing, processing, and displaying this data requires specialized expertise and is often tedious and repetitive. Leveraging LLMs to develop an automated workflow presents a highly promising solution. However, LLMs are not adept at handling complex numerical computations and table manipulations and are also constrained by a limited context budget. Based on this, we propose Data-Copilot, a data analysis agent that autonomously performs querying, processing, and visualization of massive data tailored to diverse human requests. The advancements are twofold: First, it is a code-centric agent that receives human requests and generates code as an intermediary to handle massive data, which is quite flexible for large-scale data processing tasks. Second, Data-Copilot involves a data exploration phase in advance, which explores how to design more universal and error-free interfaces for real-time response. Specifically, it actively explores data sources, discovers numerous common requests, and abstracts them into many universal interfaces for daily invocation. When deployed in real-time requests, Data-Copilot only needs to invoke these pre-designed interfaces, transforming raw data into visualized outputs (e.g., charts, tables) that best match the user's intent. Compared to generating code from scratch, invoking these pre-designed and compiler-validated interfaces can significantly reduce errors during real-time requests. Additionally, interface workflows are more efficient and offer greater interpretability than code. We open-sourced Data-Copilot with massive Chinese financial data, such as stocks, funds, and news, demonstrating promising application prospects.

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Insightful Analysis of "Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow"

The paper "Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow" introduces a novel system named Data-Copilot, focused on addressing the intricacies of data management, processing, and visualization using LLMs. In the context of burgeoning data across multiple industries such as finance and energy, the authors propose an innovative integration of LLMs to autonomously handle these data-related tasks, thus minimizing human intervention and leveraging the computational prowess of AI.

Core Contributions

The primary contribution of this work is the Data-Copilot system, which autonomously designs and deploys interface tools tailored for data acquisition, processing, and visualization. The system comprises two key processes: Interface Design and Interface Dispatch. These processes are distinctly outlined, ensuring that Data-Copilot can manage complex requests with minimal human input.

  1. Interface Design: This phase involves the creation of versatile tools that allow for a broad spectrum of data management capabilities. By employing a self-request mechanism, the system iteratively generates and refines interfaces, abstracting complex data queries into manageable tasks.
  2. Interface Dispatch: Upon receiving a user request, the system autonomously constructs a workflow utilizing the designed interfaces. This involves a detailed analysis of user intent and the deployment of a computational plan that can involve sequential, parallel, or looping workflows.

Numerical Results and Claims

The authors underscore the efficacy of Data-Copilot through its application in the Chinese financial market. Specifically, they demonstrate its ability to handle stock, fund, and economic data with a focus on scalability and adaptability. The paper claims that the system can autonomously transform vast raw data into user-friendly outputs, such as tables and graphs, aligning perfectly with user intent.

Implications and Future Developments

Practically, Data-Copilot reduces the burden of tedious data handling tasks, allowing experts to focus on critical decision-making aspects. The tool's ability to expand its interface libraries with emerging data points to its scalability, ensuring it remains relevant as data sources and user needs evolve.

Theoretically, this work opens avenues for the development of AI systems capable of crafting sophisticated data science workflows without direct human scripting. Future developments could see enhanced online interface design, thus integrating real-time data input and system feedback to further refine process automation.

In summary, Data-Copilot exemplifies a sophisticated application of LLMs, presenting a beneficial tool for industries inundated with data. Its automated approach to data handling signifies a step toward more autonomous AI systems capable of undertaking significant data-intensive tasks with minimal oversight.

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Authors (4)
  1. Wenqi Zhang (41 papers)
  2. Yongliang Shen (47 papers)
  3. Weiming Lu (54 papers)
  4. Yueting Zhuang (164 papers)
Citations (42)
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GitHub

  1. GitHub - zwq2018/Data-Copilot: Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow (1,307 stars)