Logical Lease Litigation: Prolog and LLMs for Rental Law Compliance in New York

2025-02-11

Electronic Proceedings in Theoretical Computer Science

AI Summary

This paper addresses the challenges of manual, low-explainability, and hallucination-prone legal compliance analysis in New York State landlord-tenant disputes. We propose LogicLease, a novel hybrid system integrating large language models (LLMs) with Prolog. In this architecture, the LLM performs natural language information extraction, while Prolog executes symbolic, verifiable legal reasoning—effectively decoupling semantic understanding from rule-based inference. Compared to end-to-end LLM approaches, LogicLease significantly enhances reasoning controllability, hallucination resistance, and traceability of statutory citations. Evaluated on real-world cases, LogicLease achieves 100% accuracy in compliance judgments, with an average response time of 2.57 seconds and full support for granular, article-level legal grounding.

Technology Category

• Natural Language Processing: (Large) Language Models
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Legal cases require careful logical reasoning following the laws, whereas interactions with non- technical users must be in natural language. As an application combining logical reasoning using Prolog and natural language processing using large language models (LLMs), this paper presents a novel approach and system, LogicLease, to automate the analysis of landlord-tenant legal cases in the state of New York. LogicLease determines compliance with relevant legal requirements by analyzing case descriptions and citing all relevant laws. It leverages LLMs for information extraction and Prolog for legal reasoning. By separating information extraction from legal reasoning, LogicLease achieves greater transparency and control over the legal logic applied to each case. We evaluate the accuracy, efficiency, and robustness of LogicLease through a series of tests, achieving 100% accuracy and an average processing time of 2.57 seconds. LogicLease presents advantages over state-of-the-art LLM- based legal analysis systems by providing clear, step-by-step reasoning, citing specific laws, and distinguishing itself by its ability to avoid hallucinations - a common issue in LLMs.

Problem

• Automates legal case analysis
• Combines Prolog and LLMs
• Ensures New York rental law compliance

Innovation

• Prolog for legal reasoning
• LLMs for information extraction
• Automated landlord-tenant case analysis