LogicLease: Combining Prolog and LLMs for Legal Reasoning in Rental Law
Legal AI systems face a fundamental challenge: they must process natural language while applying precise logical reasoning. Most current approaches rely entirely on large language models, which can hallucinate legal conclusions or fail to cite specific statutes. LogicLease offers a different solution by separating information extraction from legal reasoning.
The Hybrid Architecture Advantage
LogicLease combines two complementary technologies. Large language models extract facts from case descriptions written in natural language. Prolog then applies formal legal rules to these extracted facts, determining compliance with New York rental law.
This separation provides three key benefits:
- Transparency: Each reasoning step follows explicit logical rules
- Accuracy: Formal logic prevents hallucinated legal conclusions
- Auditability: The system cites specific laws for each determination
How LogicLease Works
The system processes landlord-tenant cases through a two-stage pipeline:
Stage 1: Information Extraction The LLM component reads case descriptions and extracts structured facts like lease terms, tenant complaints, and landlord actions. This stage handles the complexity of natural language understanding.
Stage 2: Legal Reasoning Prolog rules encode New York rental law requirements. The system applies these rules to extracted facts, determining violations and citing relevant statutes. This stage ensures logical consistency.
For example, when analyzing a case about security deposit returns, the LLM extracts the deposit amount and return timeline. Prolog then checks these facts against New York’s security deposit laws to determine compliance.
Performance Results
Testing on landlord-tenant cases demonstrates LogicLease’s effectiveness:
- 100% accuracy on legal determinations
- 2.57 seconds average processing time
- Zero hallucinations in legal reasoning
The system correctly identified all legal violations while providing step-by-step explanations and statute citations.
Implementation Considerations
Building similar hybrid systems requires careful attention to the interface between components. The LLM must extract facts in formats that Prolog can process. Legal rules need encoding as formal logic statements. Error handling becomes critical when natural language processing fails.
The approach works best for well-defined legal domains with clear statutory requirements. More subjective areas of law may require different techniques.
Beyond Rental Law
LogicLease demonstrates how hybrid architectures can address AI limitations in legal reasoning. The pattern of separating information extraction from logical inference applies to other domains requiring both natural language understanding and formal reasoning.
Legal tech developers can adapt this approach to contract analysis, regulatory compliance, and case law research. The key insight remains: combine the strengths of neural and symbolic AI rather than relying on either alone.
The system shows that transparent, accurate legal AI is achievable when you match the right tool to each part of the problem.