Fine-Tuning Large Language Models for Compliance Checks

The auditing of financial documents, traditionally a labor-intensive task, is a promising field of application for Artificial Intelligence. Recommendation systems are capable of suggesting the most relevant passages from financial reports that meet accounting standards’ legal requirements. However, testing if the compliance requirements are satisfied is a non-trivial task. In this work, we tackle this problem from two directions. Our first approach leverages Large Language Models which we fine-tune specifically f or compliance checks. Our results show an improvement in performance over the generic baseline {LLMs}. A disadvantage of {LLMs} is that they result in high inference costs. For this reason, we explore a second approach in which we use smaller models that come with reduced running costs. Despite their smaller size, these models also show promising predictive performance.

  • Published in:
    2024 {IEEE} International Conference on Big Data ({BigData})
  • Type:
    Inproceedings
  • Authors:
    Bell, Thiago; Leonhard, David; Bashir, Ali Hamza; Dilmaghani, Tim; Khaled, Mohamed; Warning, Ulrich; Loitz, Rüdiger; Halscheidt, Sandra; Birr, Jana; Berger, Armin; Sifa, Rafet; Berghaus, David
  • Year:
    2024
  • Source:
    https://ieeexplore.ieee.org/abstract/document/10825159/authors#authors

Citation information

Bell, Thiago; Leonhard, David; Bashir, Ali Hamza; Dilmaghani, Tim; Khaled, Mohamed; Warning, Ulrich; Loitz, Rüdiger; Halscheidt, Sandra; Birr, Jana; Berger, Armin; Sifa, Rafet; Berghaus, David: Fine-Tuning Large Language Models for Compliance Checks, 2024 {IEEE} International Conference on Big Data ({BigData}), 2024, 8790--8792, December, https://ieeexplore.ieee.org/abstract/document/10825159/authors#authors, Bell.etal.2024a,

Associated Lamarr Researchers

Prof. Dr. Rafet Sifa

Prof. Dr. Rafet Sifa

Principal Investigator Hybrid ML to the profile