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Auditing financial documents is a very tedious and time-consuming process. Asof today, it can already be simplified by employing AI-based solutions torecommend relevant text passages from a report for each legal requirement ofrigorous accounting standards. However, these methods need to be fine-tunedregularly, and they require abundant annotated data, which is often lacking inindustrial environments. Hence, we present ZeroShotALI, a novel recommendersystem that leverages a state-of-the-art large language model (LLM) inconjunction with a domain-specifically optimized transformer-basedtext-matching solution. We find that a two-step approach of first retrieving anumber of best matching document sections per legal requirement with a customBERT-based model and second filtering these selections using an LLM yieldssignificant performance improvements over existing approaches.

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