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Various industries such as finance, meteorology, and energy generate vastamounts of heterogeneous data every day. There is a natural demand for humansto manage, process, and display data efficiently. However, it necessitateslabor-intensive efforts and a high level of expertise for these data-relatedtasks. Considering that large language models (LLMs) have showcased promisingcapabilities in semantic understanding and reasoning, we advocate that thedeployment of LLMs could autonomously manage and process massive amounts ofdata while displaying and interacting in a human-friendly manner. Based on thisbelief, we propose Data-Copilot, an LLM-based system that connects numerousdata sources on one end and caters to diverse human demands on the other end.Acting like an experienced expert, Data-Copilot autonomously transforms rawdata 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-timeresponse, it automatically deploys a concise workflow by invoking correspondinginterfaces step by step for the user's request. The interface design anddeployment processes are fully controlled by Data-Copilot itself, without humanassistance. Besides, we create a Data-Copilot demo that links abundant datafrom different domains (stock, fund, company, economics, and live news) andaccurately respond to diverse requests, serving as a reliable AI assistant.

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