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

arXiv: https://arxiv.org/abs/2308.06111 · HTML: https://mlsanity.com/paper/2308.06111/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 11 Aug 2023.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021)
- [ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms](https://mlsanity.com/paper/1807.05614/) (2018)
- [KPI-BERT: A Joint Named Entity Recognition and Relation Extraction Model for Financial Reports](https://mlsanity.com/paper/2208.02140/) (2022)
- [KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents](https://mlsanity.com/paper/2210.09163/) (2022)
- [Towards automating Numerical Consistency Checks in Financial Reports](https://mlsanity.com/paper/2211.06112/) (2022)
- [Zero-Shot Text Matching for Automated Auditing using Sentence Transformers](https://mlsanity.com/paper/2211.07716/) (2022)

## Built on by (most cited first, 5 of 5)

- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [Flipping Knowledge Distillation: Leveraging Small Models' Expertise to Enhance LLMs in Text Matching](https://mlsanity.com/paper/2507.05617/) (2025)
- [Boardwalk Empire: How Generative AI is Revolutionizing Economic Paradigms](https://mlsanity.com/paper/2410.15212/) (2024)
- [INACIA: Integrating Large Language Models in Brazilian Audit Courts: Opportunities and Challenges](https://mlsanity.com/paper/2401.05273/) (2024)
- [On the Impact of Anonymization on the Performance of Large Language Models](https://mlsanity.com/paper/2609.11335/) (2026)

## Abstract (arXiv)

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.