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This paper investigates the synergistic integration of symbolic expert systems with generative AI to improve document automation, specifically within legal contexts. Preliminary experiments demonstrate how large language models can effectively identify and correct errors in texts generated by non-experts when combined with traditional reasoning methods. The work highlights a practical pathway for enhancing access to justice by bridging structured logic with the flexibility of modern neural networks.
In With the Old: Enhancing 'Classical' Document Automation with Generative AI
arXiv:2610.07480v1 Announce Type: cross Abstract: 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.
Abstract: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.
10 pages. AI for Access to Justice Workshop at ICAIL 2025
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2610.07480 [cs.AI]
(or arXiv:2610.07480v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.07480 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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