millat/BDLawCorpus-Dataset-V1

BDLawCorpus-1 Safe Instruction & RAG Dataset

This dataset provides the first comprehensive, hallucination-resistant, and instruction-tuned dataset for training Legal AI assistants for the laws of Bangladesh. It translates complex legal statutes into accessible, easy-to-understand Bengali (সহজ বাংলা) while embedding strict safety guardrails.

Dataset Breakdown

This dataset repository contains four distinct parts, allowing seamless integration into RAG pipelines and Finetuning (LoRA):

1. rag_passages.jsonl (15.4k+ chunks): Token-aware, deduplicated passages of all 1,570 active and historical Bangladeshi acts. Ideal for Semantic Search/Vector Databases (Pinecone, ChromaDB).
2. finetune.jsonl (4.7k+ pairs): Instruction, Context, and Response pairs designed to train LLMs (Llama-3, Qwen) into the "Legal Advisor (লিয়্যাল এডভাইজার)" persona. Each response includes a mandated disclaimer and cites specific evidence bounds.
3. qa_pairs.jsonl: Curated Question-Answer chunks mapping back to act purposes. Includes "Adversarial Refusals" directly training the model to admit when it lacks context instead of hallucinating.
4. metadata.csv: A dense metadata framework capturing mentions_amendment, repealed, and specifically replaced_by rules, preventing the RAG system from serving outdated advice.

Dataset Structure & Examples

1. RAG Passages (rag_passages.jsonl)

Each chunk represents a semantic slice of a legal act, carrying provenance metadata to trace exactly where the text originated.

{
  "id": "1_chunk_0",
  "title": "The Districts Act, 1836",
  "act_file": "1.json",
  "start": 0,
  "end": 324,
  "text": "The Districts Act, 1836 ( ACT NO. XXI OF 1836 ) Power to create new Districts It shall be lawful for the Government, by notification in the official Gazette, to create new districts in any part of Bangladesh."
}

2. Finetuning Pairs (finetune.jsonl)

Contains the prompt (instruction), the raw legal text (context), the structured AI behavior (response), and traceability (provenance).

{
  "instruction": "If a rural or common citizen asks for specific legal guidance...",
  "context": "",
  "response": {
    "disclaimer": "আমি একজন এআই লিগ্যাল এডভাইজার, পেশাদার আইনজীবী নই। আইনি তথ্যের উপর ভিত্তি করে আমি আপনাকে সাধারণ পরামর্শ দিচ্ছি।",
    "info": "আইনের প্রাসঙ্গিক ধারা সহজ বাংলায় বুঝিয়ে বলুন...",
    "next_steps": "প্রয়োজনে স্থানীয় কোনো আইনজীবী বা লিগ্যাল এইড (Legal Aid) এর সাথে যোগাযোগ করার পরামর্শ দিন।"
  },
  "provenance": {
    "act_file": "1.json"
  }
}

Purpose

The primary purpose of this dataset is to provide a safe, structurally intact, and linguistically accessible foundation for Legal AI Applications in Bangladesh. It deliberately bounds the AI to act as an advisory entity rather than an authoritative court, mitigating the risk of legal hallucination through embedded disclaimers and specific refusal training.

How to Use

You can easily load the various segments of the dataset using the Hugging Face datasets library.

Loading the Data

from datasets import load_dataset

# 1. Load the RAG passages for Vector Search Embeddings
rag_dataset = load_dataset("millat/BDLawCorpus-Dataset-V1", data_files="rag_passages.jsonl")

# 2. Load the Finetuning pairs for Instruction Tuning (LoRA/QLoRA)
finetune_dataset = load_dataset("millat/BDLawCorpus-Dataset-V1", data_files="finetune.jsonl")

Use Case 1: Retrieval-Augmented Generation (RAG)

Pass the rag_dataset into a multilingual embedding model (e.g., sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) and store the vectors in ChromaDB, Pinecone, or FAISS. The chunks are already optimized for standard context windows and maintain 100% provenance back to the original acts.

Use Case 2: Instruction Finetuning

Use the finetune_dataset with frameworks like Unsloth or Hugging Face TRL to instruction-tune foundational models like Llama-3-8B or Qwen-2.5. The dataset structure already separates specific instruction, context, and structured JSON response fields.

Link to Codebase & Methodology

The full scraping, chunking, and dataset-safety alignment architecture is open-source.

• GitHub Repository: https://github.com/codermillat/BDLawCorpus

Next Steps: Model Finetuning

This dataset paves the way for the forthcoming BDLaw-Instruct model, a finetuned framework optimizing rural accessibility to legal boundaries in Bangladesh.

Empirical Quality Evaluation for BDLawCorpus-1

This structured evidence is generated to satisfy formal Academic Research criteria ensuring dataset validity across Quantitative, Safety, and Structural dimensions.

A. Quantitative Statistics (Corpus Size & Distribution)

• Total RAG Chunks: 15428 passages generated across the corpus.
• RAG Passage Distribution: Average length = 2581.55 characters. Max length = 10934 characters (Fits 4K/8K LLM windows).
• Instruction-Tuning Scenarios: 4710 pairs extracted.
• Instruct Response Length: Average length = 260.95 characters.

B. Alignment & Safety Guardrails (Prompt Engineering Evidence)

• Disclaimer Injection Rate: 1570 instances (33.33% of finetuning pairs) explicitly carry a predefined safety disclaimer (e.g. "আমি একজন এআই লিগ্যাল এডভাইজার...").
• Adversarial Refusal Embeddings: 1570 instances (33.33%) strictly guide the model to safely refuse or bound unauthorized advice.

C. Structural Integrity & Provenance (Traceability)

• Data Pollution (HTML/JS Trace): 0 chunks (0.00%) contained polluted HTML boundaries (0% indicates perfect script stripping).
• Provenance Linkage:
  • RAG Traceability: 100.00% of passages contain mapping.
  • Instruct Traceability: 100.00% of finetuning pairs contain act_file mapping.