IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2310.11249.html
   My bibliography  Save this paper

Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle

Author

Listed:
  • Xu Yang
  • Xiao Yang
  • Weiqing Liu
  • Jinhui Li
  • Peng Yu
  • Zeqi Ye
  • Jiang Bian

Abstract

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.

Suggested Citation

  • Xu Yang & Xiao Yang & Weiqing Liu & Jinhui Li & Peng Yu & Zeqi Ye & Jiang Bian, 2023. "Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle," Papers 2310.11249, arXiv.org.
  • Handle: RePEc:arx:papers:2310.11249
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2310.11249
    File Function: Latest version
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Haotian Chen & Xinjie Shen & Zeqi Ye & Wenjun Feng & Haoxue Wang & Xiao Yang & Xu Yang & Weiqing Liu & Jiang Bian, 2024. "Towards Data-Centric Automatic R&D," Papers 2404.11276, arXiv.org, revised Jul 2024.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2310.11249. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.