Leveraging Large Language Models for Few-Shot {KPI} Extraction from Financial Reports

We explore the use of Large Language Models ({LLMs}) for automating the extraction of Key Performance Indicators ({KPIs}) from diverse financial reports without any additional fine-tuning. We focus on evaluating various proprietary and open-source {LLMs} to address the joint named entity recognition and relation extraction tasks essential for accurately linking {KPIs} to their corresponding values and attributes. Our study highlights the technical challenges involved in the extraction process and presents a comprehensive evaluation of the models’ effectiveness. Our results reveal significant insights into handling these {LLMs} in such a crucial environment and showcase the transformative potential of {LLMs} in enhancing financial analysis and decision-making.

  • Published in:
    2024 {IEEE} International Conference on Big Data ({BigData})
  • Type:
    Inproceedings
  • Authors:
    Deußer, Tobias; Zhao, Cong; Uedelhoven, Daniel; Sparrenberg, Lorenz; Hillebrand, Lars; Bauckhage, Christian; Sifa, Rafet
  • Year:
    2024
  • Source:
    https://ieeexplore.ieee.org/abstract/document/10825458

Citation information

Deußer, Tobias; Zhao, Cong; Uedelhoven, Daniel; Sparrenberg, Lorenz; Hillebrand, Lars; Bauckhage, Christian; Sifa, Rafet: Leveraging Large Language Models for Few-Shot {KPI} Extraction from Financial Reports, 2024 {IEEE} International Conference on Big Data ({BigData}), 2024, 4864--4868, December, https://ieeexplore.ieee.org/abstract/document/10825458, Deusser.etal.2024c,

Associated Lamarr Researchers

Kopie von LAMARR Person 500x500 1 - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Christian Bauckhage

Director to the profile
Prof. Dr. Rafet Sifa

Prof. Dr. Rafet Sifa

Principal Investigator Hybrid ML to the profile