Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909615344582656 |
|---|---|
| author | Zhang, Wenqi Shen, Yongliang Tan, Zeqi Hou, Guiyang Lu, Weiming Zhuang, Yueting |
| author_facet | Zhang, Wenqi Shen, Yongliang Tan, Zeqi Hou, Guiyang Lu, Weiming Zhuang, Yueting |
| contents | 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 large language models (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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_07209 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow Zhang, Wenqi Shen, Yongliang Tan, Zeqi Hou, Guiyang Lu, Weiming Zhuang, Yueting Computation and Language Artificial Intelligence Computational Engineering, Finance, and Science 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 large language models (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. |
| title | Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow |
| topic | Computation and Language Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2306.07209 |