LawAssist Research Lab
Deep dives into the NLP, RAG, and LLM fine-tuning pipelines built to understand Indian jurisprudence. We build transparently, sharing our experiments and metrics.
Grounding LLMs in Indian Kanoon: Minimizing Precedent Hallucinations
General-purpose models frequently hallucinate case citations (e.g. creating fake SCC or AIR citations). We outline our dual-embedding validation protocol that grounds model reasoning in official Indian Kanoon documents, verifying every link prior to showing results.
Fine-Tuning Qwen 3B for Section & Act Parameter Extraction
We evaluated Llama 3.2 3B and Qwen 2.5 3B models on extracting statutory parameters from raw pleadings. By fine-tuning Qwen on a curated corpus of 12,000 Indian petitions, we optimized the model to flag Section 138 NI Act, Section 18 RERA, and Section 420 IPC issues with high precision.
High Court PDF Parsing & Clean Text Extraction Pipelines
High Court petitions and orders frequently contain multi-column formats, bilingual scripts (English and Devnagari/Tamil/etc.), and scanning artifacts. We built a custom PyMuPDF and pdfplumber parsing pipeline that strips page headers and isolates operative facts.
Legal Re-ranking: Transitioning from Keyword to Semantic Relevance
Traditional databases index matching keywords, failing when advocates use synonymous terms (e.g. "delay in flat" vs "non-delivery of possession"). We discuss our LLM re-ranking model which parses Kanoon search output and ranks judgments by fact similarity.
Why we host local open-source models
Using external commercial LLM APIs exposes client case facts to foreign data loops. To ensure complete privacy and zero data leakage, we deploy self-hosted models (Qwen 3B, Llama 3.2) in secure, private containers. No external companies ever see your pleadings.
Read Our Security Protocols