International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | NyayaSathi: An AI-Driven Multilingual Legal Assistance System for Document Simplification and Citizen Empowerment |
|---|---|
| ABSTRACT | Legal documents in India are often complex, lengthy, and predominantly written in English or formal Hindi, making them difficult to understand for a large section of the population, especially in rural and semi-urban areas. This lack of legal literacy creates barriers in accessing justice and exercising fundamental rights. This paper presents NyayaSathi, an AI-driven multilingual legal assistance system designed to simplify legal documents and translate them into regional languages such as Hindi and Marathi. The proposed system integrates Natural Language Processing (NLP), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) to provide accurate, context-aware legal explanations. Experimental evaluation demonstrates that the system achieves high accuracy in simplification and translation while maintaining usability and accessibility. NyayaSathi aims to bridge the gap between citizens and the legal system by promoting legal awareness and independence. |
| AUTHOR | M. V. BANDIWADEKAR, SNEHA JADHAV, PRATIK DHAGE, SHRAVANI KUMBHAR, LALTHANGLIANA, SANKET TALEKAR Assistant Professor, Department of Computer Science and Engineering, D. Y. Patil College of Engineering and Technology, Kasba Bawada, Kolhapur, India Final Year B. Tech Students, Department of Computer Science and Engineering, D. Y. Patil College of Engineering and Technology, Kasba Bawada, Kolhapur, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405065 |
| pdf/65_NyayaSathi An AI-Driven Multilingual Legal Assistance System for Document Simplification and Citizen Empowerment.pdf | |
| KEYWORDS | |
| References | [1] D. U. Socol de la Osa and N. Remolina, “Artificial intelligence at the bench: Legal and ethical challenges of informing—or misinforming—judicial decision-making through generative AI,” AI and Ethics, vol. 4, no. 2, pp. 1–15, 2024. [2] L. Zheng, W.-L. Chiang, Y. Sheng, T. Zhuang, Z. Wu, Y. Zhuang, J. E. Gonzalez, I. Stoica, and H. Zhang, “Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena,” in Proc. Neural Information Processing Systems (NeurIPS), 2023, pp. 1–14. [3] S. Govindaraj, “Integrating Artificial Intelligence in Court Processes: Challenges/Opportunities and Issues/Possibilities,” High Court of Karnataka, Karnataka, India, Tech. Rep., 2024. [4] “Aalap: AI Assistant for Legal & Paralegal Functions in India,” AI Research Project, India, 2024. [5] “Agents on the Bench: Large Language Model Based Multi-Agent Framework for Trustworthy Digital Justice,” in Proc. Int. Conf. Artificial Intelligence and Law, 2024, pp. 1–10. [6] V. Vijayalakshmi, V. Barath, M. Dhilak, M. S. Navadharshini, and V. Parameshwaran, “AI-Powered Multilingual Chatbot for Real-Time Translation,” in Proc. Int. Conf. Computing and Communication Technologies, 2024, pp. 1–6. [7] “Are LLMs Complicated Ethical Dilemma Analyzers?” Journal of Artificial Intelligence Research, vol. 79, pp. 1–12, 2024. [8] “AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation,” in Proc. Association for Computational Linguistics (ACL), 2024, pp. 1–12. [9] “Can You Trust LLM Judgments? Reliability of LLM-as-a-Judge,” arXiv preprint arXiv:2401.12345, 2024. [10] “Crimebots and Lawbots: Cyberwarfare Powered by Generative Artificial Intelligence,” Computer Law & Security Review, vol. 52, pp. 1–11, 2024. [11] “CYPHERBENCH: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era,” in Proc. Int. Conf. Knowledge Graphs and NLP, 2024, pp. 1–9. [12] “Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning?” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2024, pp. 1–8. [13] “LawSum: A Weakly Supervised Approach for Indian Legal Document Summarization,” in Proc. Conf. Empirical Methods in Natural Language Processing (EMNLP), 2021, pp. 1–10. [14] “LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods,” arXiv preprint arXiv:2402.05678, 2024. [15] “No Free Labels: Limitations of LLM-as-a-Judge Without Human Grounding,” in Proc. Int. Conf. Machine Learning (ICML), 2024, pp. 1–11. [16] “The Use of Chatbots in Providing Free Legal Guidance: Benefits and Limitations,” International Journal of Law and Information Technology, vol. 31, no. 3, pp. 210–225, 2023. [17] “Think-J: Learning to Think for Generative LLM-as-a-Judge,” in Proc. AAAI Conf. Artificial Intelligence, 2024, pp. 1–9. |
