This project is using RAG (Retrieval-Augmented Generation) to retrieve relevant legal data from stored database and then generate the response based on that data, which helps in giving more accurate and meaningful answers instead of general responses.
AI Legal Buddy is an AI-powered legal information assistant designed to help users understand Indian laws in a simple and accessible way. The system allows users to enter their legal queries through text or voice input. The frontend is developed using React, TypeScript, Tailwind CSS, and Vite, providing a responsive and user-friendly interface. The backend is implemented using Supabase Edge Functions, which process user queries. In this project, we are using RAG (Retrieval- Augmented Generation) to retrieve relevant legal data from stored database and then generate the response based on that data. This helps in giving more accurate and meaningful answers instead of general responses. The system analyzes the query, identifies related Indian Acts and Sections, checks the seriousness of the issue, and provides structured responses with useful steps. The system also supports multiple languages to improve accessibility for users from different regions. By combining modern web technologies with RAG-based approach, the project aims to make legal information more understandable and easy to access for common people.
Keywords: artificial intelligence; Retrieval-Augmented Generation; Legal Information Sys- tems; Multi-Agent Systems.
Tadiparti.Venu, Pakki Bhargava Shanmukha Sai, Lotheti Pravalika et al.· International Journal of Tec...· 0 citations
This study provides an AI- Based document analyzer with a question-answer system that makes use of Natural Language Processing approaches that is affordable, scalable, and suitable for business, education, and research.
Radhika Sharma, Devraj Gautam· Revolutionary Advances in Co...· 0 citations
This study presents the development of an Artificial Intelligence (AI)-based legal assistant using the Retrieval-Augmented Generation (RAG) architecture to provide legal assistance to citizens of the Republic of Kazakhstan. The proposed solution is designed to generate accurate, evidence-based responses to user queries using the regulatory legal acts of the Republic of Kazakhstan as the primary source of information. A legal corpus comprising 101,000 legislative documents and court decisions, with approximately 77 million tokens in Kazakh and Russian, was constructed to support the retrieval component of the system. To identify the most effective semantic retrieval method, three multilingual embedding models—Multilingual-E5-Large, BGE-M3, and KazEmbed-V5—were evaluated for vector search. The experimental results showed retrieval accuracies of 87.6%, 76.8%, and 83.3%, respectively. The GPT-5.4 and Llama-4-Scout-17B-16E-Instruct large language models were used to generate legal reasoning and responses based on documents retrieved through semantic search. The quality of the generated responses was evaluated using two complementary approaches. First, legal experts assessed the factual correctness and legal validity of the answers. Second, automatic evaluation was performed using word-level F1, BLEU, ROUGE, and BERTScore-F1 metrics. Among all evaluated configurations, GPT-5.4 combined with Multilingual-E5-Large achieved the highest overall accuracy (88.5%), whereas Llama-4-Scout-17B-16E-Instruct combined with KazEmbed-V5 achieved an accuracy of 83.6%. Based on the proposed architecture and the selected semantic retrieval and language models, an AI legal assistant was developed and integrated into the “Adal Azamat” legal services platform providing users in Kazakhstan with practical access to AI-assisted legal consultation.
N. Mukazhanov, Z. Alibiyeva, A. Akhmediyarova et al.· Computers· 0 citations
This comprehensive study introduces an advanced
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rtificial Intelligence for
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ndian
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egal
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uestion
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nswering or system tailored for the Indian legal context. leverages a variety of embedding and generative models, including the latest Large Language Models (LLMs), to address the unique challenges posed by the intricate and diverse nature of Indian legal texts, to enhance the accuracy and reliability of legal question responses. We conducted rigorous evaluations using both lexical and semantic metrics that are enriched by expert legal feedback to ensure relevance and accuracy. Our findings underscore the effectiveness of the Retrieval-Augmented Generation (RAG) paradigm in improving answer quality, particularly in complex legal domains. Additionally, we explored the performance on standardized tests such as the All India Bar Exam (AIBE), thus providing a robust benchmark for a practical application. Under the study’s evaluation protocol, some AI-generated responses received higher ratings than the available reference answers, particularly when they contained accurate and relevant supporting detail. This finding is specific to the evaluated dataset and rating criteria and should not be interpreted as evidence that the models generally outperform qualified legal professionals. We also discuss the challenges encountered, such as the need for precise context and the risks of model hallucination, and propose directions for future research to further refine AI capabilities in the legal field. This study aims to pave the way for enhanced legal decision-making support systems, making them more accessible and effective for legal professionals and the public alike.
S. Nigam, Shubham Kumar Mishra, Noel Shallum et al.· Artificial Intelligence and...· 1 citation
Legal decisions on asylum applications consist of long, complex, and heterogeneous documents, covering narrative applicant interviews, original decisions, and additional supporting materials. If an application is rejected, a critical question in processing an appeal is whether the credibility of the information in the original application was a factor that determined the original decision. In this paper, we present the QUEST system (Query and Extraction System for Topics) to extract and identify factors relating to credibility assessments in two datasets of Danish asylum application appeals. QUEST frames this problem as an information retrieval task, combining synthetic query generation, topic extraction, and relevance assessment to identify information related to credibility indicators in appeals board application materials. In addition to standard retrieval evaluation metrics, we propose a new type of domain-specific assessments distinct from the traditional relevance to evaluate the performance of the tested systems with respect to credibility factors. In this way, we obtain insights about how well automatic methods can return answers for different types of indicators appearing in asylum appeals. Our results indicate that there is an increased challenge when estimating performance using credibility-based relevance assessments, thus pointing to the difficulty of the task.
Maria Vlachou, Anna Murphy Høgenhaug, Mohammad N. S. Jahromi et al.· 0 citations
The design realization and evaluation of an Automated Summarization Tool (AST) is presented which is a document intelligence platform based on google gemini 2.5 flash that outperforms the strongest fine-tuned transformer baselines (PEGASUS, BART) by ~14 points and is clearly ahead of BERTSUM-ext (a strong transformer baseline), Pointer-Generator Network, TextRank.
K. Kumar, A. Amandeep, Dharmender Kumar et al.· International Journal of Inn...· 0 citations
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