# In With the Old: Enhancing 'Classical' Document Automation with Generative AI

arXiv: https://arxiv.org/abs/2610.07480 · HTML: https://mlsanity.com/paper/2610.07480/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 5 Oct 2026.

## Builds on (most cited first, 6 of 6)

- [Explaining Legal Concepts with Augmented Large Language Models (GPT-4)](https://mlsanity.com/paper/2306.09525/) (2023)
- [Unlocking Practical Applications in Legal Domain: Evaluation of GPT for Zero-Shot Semantic Annotation of Legal Texts](https://mlsanity.com/paper/2305.04417/) (2023)
- [Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies](https://mlsanity.com/paper/2310.18729/) (2023)
- [Robots in the Middle: Evaluating LLMs in Dispute Resolution](https://mlsanity.com/paper/2410.07053/) (2024)
- [Getting in the Door: Streamlining Intake in Civil Legal Services with Large Language Models](https://mlsanity.com/paper/2410.03762/) (2024)
- [Analyzing Images of Legal Documents: Toward Multi-Modal LLMs for Access to Justice](https://mlsanity.com/paper/2412.15260/) (2024)

## Abstract (arXiv)

Software-based legal assistance systems have leveraged many different forms of knowledge representation and reasoning. This article explores how document automation services rooted in expert system style and other symbolic approaches can usefully enhance and be enhanced by current generative AI approaches. We discuss the possible benefits and challenges, and report on preliminary experiments in using large language models to identify and fix issues in texts written by laypeople.