  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.
