Improving Zero-Shot Text Matching for Financial Auditing with Large Language Models

Auditing financial documents is a very tedious and time-consuming process. As of today, it can already be simplified by employing AI-based solutions to recommend relevant text passages from a report for each legal requirement of rigorous accounting standards. However, these methods need to be fine-tuned regularly, and they require abundant annotated data, which is often lacking in industrial environments. Hence, we present ZeroShotALI, a novel recommender system that leverages a state-of-the-art large language model (LLM) in conjunction with a domain-specifically optimized transformer-based text-matching solution. We find that a two-step approach of first retrieving a number of best matching document sections per legal requirement with a custom BERT-based model and second filtering these selections using an LLM yields significant performance improvements over existing approaches.

  • Published in:
    ACM Symposium on Document Engineering
  • Type:
    Inproceedings
  • Authors:
    Hillebrand, Lars; Berger, Armin; Deußer, Tobias; Dilmaghani, Tim; Khaled, Mohamed; Kliem, Bernd; Loitz, Rüdiger; Pielka, Maren; Leonhard, David; Bauckhage, Christian; Sifa, Rafet
  • Year:
    2023

Citation information

Hillebrand, Lars; Berger, Armin; Deußer, Tobias; Dilmaghani, Tim; Khaled, Mohamed; Kliem, Bernd; Loitz, Rüdiger; Pielka, Maren; Leonhard, David; Bauckhage, Christian; Sifa, Rafet: Improving Zero-Shot Text Matching for Financial Auditing with Large Language Models, ACM Symposium on Document Engineering, 2023, https://dl.acm.org/doi/10.1145/3573128.3609344, Hillebrand.etal.2023b,

Associated Lamarr Researchers

lamarr institute person Pielka Maren - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Maren Pielka

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Prof. Dr. Christian Bauckhage

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Prof. Dr. Rafet Sifa

Prof. Dr. Rafet Sifa

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