利用大型语言模型实现工业数据驱动研发周期的自动演进
Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle
AI总结：
本文利用大型语言模型加速工业数据驱动研发周期，通过评估基础要素并在量化投资场景Qlib平台上验证，实现自动演进。
摘要：
在持续不断的数字化转型浪潮中，数据驱动的解决方案正成为应对多种工业任务（如预测、异常检测、规划乃至复杂决策）的强大工具。尽管以数据为中心的研发在利用这些解决方案方面发挥了关键作用，但它常常在人力、计算和时间资源方面带来巨大成本。本文深入探讨了大型语言模型（LLMs）在加速数据驱动研发演进周期方面的潜力。通过评估数据驱动研发的基础要素，包括异构任务相关数据、多面领域知识和多样化计算功能工具，我们探索了LLMs在理解领域特定需求、生成专业想法、利用领域特定工具进行实验、解释结果以及整合以往经验以应对新挑战方面的能力。我们以量化投资研究作为工业数据驱动研发场景的典型示例，并在我们全栈开源的量化研究平台Qlib上验证了我们提出的框架，取得了令人鼓舞的结果，这为工业数据驱动研发周期的自动演进愿景提供了启示。
英文摘要：
In the wake of relentless digital transformation, data-driven solutions are emerging as powerful tools to address multifarious industrial tasks such as forecasting, anomaly detection, planning, and even complex decision-making. Although data-centric R&D has been pivotal in harnessing these solutions, it often comes with significant costs in terms of human, computational, and time resources. This paper delves into the potential of large language models (LLMs) to expedite the evolution cycle of data-centric R&D. Assessing the foundational elements of data-centric R&D, including heterogeneous task-related data, multi-facet domain knowledge, and diverse computing-functional tools, we explore how well LLMs can understand domain-specific requirements, generate professional ideas, utilize domain-specific tools to conduct experiments, interpret results, and incorporate knowledge from past endeavors to tackle new challenges. We take quantitative investment research as a typical example of industrial data-centric R&D scenario and verified our proposed framework upon our full-stack open-sourced quantitative research platform Qlib and obtained promising results which shed light on our vision of automatic evolving of industrial data-centric R&D cycle.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
