Data-Copilot: Bridging Billions of Data and Humans with Autonomous
  Workflow
Wenqi Zhang Yongliang Shen Weiming Lu Yueting Zhuang
Various industries such as finance, meteorology, and energy generate vast
amounts of heterogeneous data every day. There is a natural demand for humans
to manage, process, and display data efficiently. However, it necessitates
labor-intensive efforts and a high level of expertise for these data-related
tasks. Considering that large language models (LLMs) have showcased promising
capabilities in semantic understanding and reasoning, we advocate that the
deployment of LLMs could autonomously manage and process massive amounts of
data while displaying and interacting in a human-friendly manner. Based on this
belief, we propose Data-Copilot, an LLM-based system that connects numerous
data sources on one end and caters to diverse human demands on the other end.
Acting like an experienced expert, Data-Copilot autonomously transforms raw
data into visualization results that best match the user's intent.
Specifically, Data-Copilot autonomously designs versatile interfaces (tools)
for data management, processing, prediction, and visualization. In real-time
response, it automatically deploys a concise workflow by invoking corresponding
interfaces step by step for the user's request. The interface design and
deployment processes are fully controlled by Data-Copilot itself, without human
assistance. Besides, we create a Data-Copilot demo that links abundant data
from different domains (stock, fund, company, economics, and live news) and
accurately respond to diverse requests, serving as a reliable AI assistant.