# LawPal : A Retrieval Augmented Generation Based System for Enhanced Legal Accessibility in India

arXiv: https://arxiv.org/abs/2502.16573 · HTML: https://mlsanity.com/paper/2502.16573/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Legal and financial RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 23 Feb 2025.

## Placed in

- Retrieval-augmented generation / [Legal and financial RAG](https://mlsanity.com/rag/legal-and-financial-rag/)

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Ragas: Automated Evaluation of Retrieval Augmented Generation](https://mlsanity.com/paper/2309.15217/) (2023)
- [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024)
- [Natural Language Processing: State of The Art, Current Trends and Challenges](https://mlsanity.com/paper/1708.05148/) (2017)
- [RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model](https://mlsanity.com/paper/2402.10828/) (2024)
- [Scalable and Effective Generative Information Retrieval](https://mlsanity.com/paper/2311.09134/) (2023)
- [Bias and Fairness in Chatbots: An Overview](https://mlsanity.com/paper/2309.08836/) (2023)
- [Equality before the Law: Legal Judgment Consistency Analysis for Fairness](https://mlsanity.com/paper/2103.13868/) (2021)
- [Retrieval Augmented Generation and Representative Vector Summarization for large unstructured textual data in Medical Education](https://mlsanity.com/paper/2308.00479/) (2023)
- [Ensemble of MRR and NDCG models for Visual Dialog](https://mlsanity.com/paper/2104.07511/) (2021)
- [Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation](https://mlsanity.com/paper/1901.00171/) (2019)
- [AI and the Law: Evaluating ChatGPT's Performance in Legal Classification](https://mlsanity.com/paper/2502.12193/) (2025)

## Built on by (most cited first, 6 of 6)

- [Segment First, Retrieve Better: Realistic Legal Search via Rhetorical Role-Based Queries](https://mlsanity.com/paper/2508.00679/) (2025)
- [Build AI Assistants using Large Language Models and Agents to Enhance the Engineering Education of Biomechanics](https://mlsanity.com/paper/2511.15752/) (2025)
- [Reasoning-targeted Jailbreak Attacks on Large Reasoning Models via Semantic Triggers and Psychological Framing](https://mlsanity.com/paper/2604.15725/) (2026)
- [Train, Retrieve, or Both? A Four-Arm Head-to-Head for Correct Statutory Citation on the Ontario Residential Tenancies Act](https://mlsanity.com/paper/2606.20359/) (2026)
- [Efficient LLM Distillation for Bangladesh Legal Context: A Smartphone-Compatible Retrieval-Augmented Generation Model](https://mlsanity.com/paper/2609.24177/) (2026)
- [LawCompass: Navigating from Legal QA to Multi-Agent Deep Research with Grounded Evidence](https://mlsanity.com/paper/2610.01027/) (2026)

## Abstract (arXiv)

Access to legal knowledge in India is often hindered by a lack of awareness, misinformation and limited accessibility to judicial resources. Many individuals struggle to navigate complex legal frameworks, leading to the frequent misuse of laws and inadequate legal protection. To address these issues, we propose a Retrieval-Augmented Generation (RAG)-based legal chatbot powered by vectorstore oriented FAISS for efficient and accurate legal information retrieval. Unlike traditional chatbots, our model is trained using an extensive dataset comprising legal books, official documentation and the Indian Constitution, ensuring accurate responses to even the most complex or misleading legal queries. The chatbot leverages FAISS for rapid vector-based search, significantly improving retrieval speed and accuracy. It is also prompt-engineered to handle twisted or ambiguous legal questions, reducing the chances of incorrect interpretations. Apart from its core functionality of answering legal queries, the platform includes additional features such as real-time legal news updates, legal blogs, and access to law-related books, making it a comprehensive resource for users. By integrating advanced AI techniques with an optimized retrieval system, our chatbot aims to democratize legal knowledge, enhance legal literacy, and prevent the spread of misinformation. The study demonstrates that our approach effectively improves legal accessibility while maintaining high accuracy and efficiency, thereby contributing to a more informed and empowered society.