2023/10/17 by Xu Yang, Xiao Yang, Yang, Xu +11 · 1 citation
Decision Sciences · Materials Science · Social Sciences · #Artificial Intelligence (cs.AI) #Computational and Text Analysis Methods #FOS: Computer and information sciences #FOS: Economics and business #General Finance (q-fin.GN) #Machine Learning in Materials Science #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.2310.11249
openalex publication_date 2023/10/17 · openalex created_date 2023/10/21 · openalex updated_date 2026/07/28
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.