Falkor-IRAC: Graph-Constrained Legal AI for Indian Courts
The newly introduced framework, Falkor-IRAC, tackles issues of hallucination and reasoning errors in legal AI powered by LLMs by anchoring its outputs in structured reasoning through an IRAC (Issue, Rule, Analysis, Conclusion) knowledge graph. It processes judgments from India's Supreme Court and High Courts, transforming them into IRAC node structures that incorporate procedural state transitions, relationships to precedents, and statutory references, all stored in FalkorDB. This innovative method seeks to enhance access to justice in jurisdictions with heavy caseloads, such as India, by ensuring accurate representation of legal reasoning—something that vector-based retrieval-augmented generation fails to provide. The research paper can be found on arXiv.
Key facts
- Falkor-IRAC is a graph-constrained generation framework for Indian legal AI.
- It uses an IRAC (Issue, Rule, Analysis, Conclusion) knowledge graph.
- Judgments from the Supreme Court and High Courts of India are ingested as IRAC node structures.
- The system stores data in FalkorDB.
- It addresses hallucination and reasoning failures in LLM-based legal AI.
- The goal is to improve access to justice in high-caseload jurisdictions like India.
- The paper is published on arXiv with ID 2605.14665.
- Vector-based retrieval-augmented generation cannot faithfully represent legal reasoning.
Entities
Institutions
- Supreme Court of India
- High Courts of India
- FalkorDB
- arXiv
Locations
- India