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Domain-Partitioned Hybrid RAG for Legal Reasoning: Toward Modular and Explainable Legal AI for India
arXiv – CS AI|Rakshita Goel, S Pranav Kumar, Anmol Agrawal, Divyan Poddar, Pratik Narang, Dhruv Kumar||3 views
🤖AI Summary
Researchers developed a domain-partitioned hybrid RAG system with knowledge graphs specifically for Indian legal research, combining three specialized pipelines for Supreme Court cases, statutory texts, and penal codes. The system achieved a 70% pass rate on legal questions, nearly doubling the performance of traditional RAG-only approaches at 37.5%.
Key Takeaways
- →New hybrid RAG architecture specifically designed for Indian legal research outperforms traditional keyword-based and embedding-only systems.
- →System integrates three specialized pipelines covering Supreme Court cases, constitutional texts, and Indian Penal Code with Neo4j knowledge graphs.
- →LLM-driven orchestrator dynamically routes queries across retrieval modules to provide citation-aware responses.
- →Achieved 70% pass rate on synthetic legal benchmark, significantly outperforming 37.5% baseline RAG system.
- →Demonstrates scalable approach for domain-specific legal AI that maintains interpretability and structured reasoning.
#legal-ai#rag#knowledge-graphs#india#legal-research#llm#domain-specific#neo4j#legal-reasoning#ai-benchmarks
Read Original →via arXiv – CS AI
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