In: Proceedings of the 28th international conference on computational linguistics, pp 6229–6239 Sansone C, Sperlí G (2022) Legal information retrieval systems: State-of-the-art and open issues.

The gap

Consensus across the literature

Supporting evidence — 3 representative gaps

• Comma: A multi-task and multi-lingual dataset of constitutional verdicts (2026) · doi

  In: Proceedings of the 28th international conference on computational linguistics, pp 6229–6239 Sansone C, Sperlí G (2022) Legal information retrieval systems: State-of-the-art and open issues.

  Keywords: proceedings international conference computational linguistics sansone sperl legal information retrieval systems state open issues
• A Multi-Agent Retrieval-Augmented Generation Framework for Context-Aware Legal Document Analysis (2026) · doi

  The current framework depends on the quality of embed-dings and the availability of legal datasets. External precedent retrieval may be affected by API limitations. Future work includes expanding datasets, optimizing agent communication, and integrating domain-specific fine-tuned models. © IJARCCE This work is licensed under a Creative Commons Attribution 4.0 International License 1143 IJARCCE ISSN (O) 2278-1021, ISSN (P) 2319-5940 International Journal of Advanced Research in Computer and Communication Engineering Impact Factor 8.471Peer-reviewed & Refereed journalVol. 15, Issue 5, May 2026 DOI: 10.17148/IJARCCE.2026.155143 X. CONCLUSION This paper presents a multi-agent RAG framework tailored for legal document analysis. By integrating semantic retrieval and collaborative reasoning, the system improves grounding, interpretability, and enterprise readiness. Future work will focus on large-scale evaluation and optimization for real-world deployment. REFERENCES [1]. P. Lewis et al., “Retrieval-Augmented eneration for Knowledge-Intensive NLP Tasks,” NeurIPS, 2020. [2]. T. Brown et al., “Language Models are Few-Shot Learners,” NeurIPS, 2020. [3]. A. Vaswani et al., “Attention is All You Need,” NeurIPS, 2017. [4]. [5]. N. Reimers and I. Gurevych, “Sentence-BERT: Sentence Embeddings using Siamese BERT Networks,” J. Devlin et al., “BERT: Pre-training of Deep Bidirectional Transform-ers,” NAACL, 2019. EMNLP, 2019. [6]. Y. Wang et al., “Multi-Agent Retrieval-Augmented Generation,” ACL, 2024. [7]. Chroma Research Team, “Chroma: The AI-native Open-source Embed-ding Database,” 2023. [8]. N. Muennighoff et al., “MTEB: Massive Text Embedding Benchmark,” arXiv:2210.07316, 2023. [9]. L. Guha et al., “LegalBench: A Benchmark for Measuring Legal Reasoning,” NeurIPS Datasets and Benchmarks, 2023. [10]. S. Pipitone and G. Houir Alami, “LegalBench-RAG: Bench-marking Retrieval-Augmented Generation for Legal Reasoning,” arXiv:2408.10343, 2024. [11]. W. Wang et al., “MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression,” NeurIPS, 2020. [12]. C. Raffel et al., “Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer,” JMLR, 2020. [13]. A. Radford et al., “Language Models are Unsupervised Multitask Learners,” OpenAI Technical Report, 2019. [14]. V. Karpukhin et al., “Dense Passage Retrieval for Open-Domain Ques-tion Answering,” EMNLP, 2020. [15]. R. Thoppilan et al., “LaMDA: Language Models for Dialog Applica-tions,” arXiv:2201.08239, 2022. © IJARCCE This work is licensed under a Creative Commons Attribution 4.0 International License 1144

  Keywords: retrieval neurips legal models ijarcce datasets agent international reasoning augmented language bert text arxiv framework
• JustiFind: An Intelligent Legal Aid and Awareness System (2026) · doi

  VII. Support for Multilingual and Voice First • • Fine-Tuning Legal LLM • Case tracking and personalized legal • Mobile app and off-line capabilities • Integration with government/ NGO ecosystem VIII. REFERENCES [1] [2] [3] [4] [5] [6] [7] S. Gupta, R. Mehta, and P. Agarwal, "AI in Legal Domain: Semantic Understanding of Law Documents," in Proc. IEEE Conference on Computational Linguistics, 2021. P. Ramesh and S. Kulkarni, "Building Conversational Legal Chatbots Using Machine Learning," IEEE Transactions on Computational Social Systems, 2022. A. Kumar and V. Narayan, "Semantic Search Techniques for Legal Information Retrieval," Springer International Conference on Data Engineering and Applications, 2020. N. Desai and R. Sinha, "Bridging Legal Awareness through AI: Opportunities and Challenges," International Journal of AI Applications and Innovation, vol. 14, no. 2, pp. 45–62, 2023. C. Trivedi, S. Kumar, I. Mohd, R. Bhalla, N. A. Lone, and D. Dogra, "Leveraging AI-Driven Chatbots for Legal Literacy," IEEE Access, vol. 12, pp. 78213–78229, 2024. Nikita, E. Srivastav, A. Patel, A. Singh, R. Sharma, D. P. Rana, and R. G. Mehta, "LAWBOT: A Smart User Indian Legal Chatbot using Machine Learning Framework," in Proc. International Conference on Emerging Technologies in Computing, 2024. K. D. Ashley, Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age. Cambridge University Press, 2017. [8] M. Medvedeva, M. Vols, and M. Wieling, "Using Machine Learning to Predict Decisions of the European [9] [10] [11] Court of Human Rights," Artificial Intelligence and Law, vol. 28, no. 2, pp. 237–266, 2020. E. S. Kamarudin and M. Ismail, "Promoting Civic Engagement through Digital Platforms: The Case for Legal Awareness," International Journal of Law and Information Technology, vol. 28, no. 3, pp. 201– 224, 2020. S. P. Smith, "Combating Legal Misinformation: The Role of Technology in Public Education," Journal of Legal Communication and Rhetoric, vol. 17, no. 1, pp. 89–112, 2020. N. Reimers and I. Gurevych, "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks," in Proc. EMNLP 2019, 2019. [12] Meta AI, "LLaMA: Open and Efficient Foundation Language Models," arXiv preprint arXiv:2302.13971, 2023. www.irjmets.com @International Research Journal of Modernization in Engineering, Technology and Science [9970]

  Keywords: legal international using journal proc ieee conference machine learning technology case mehta semantic computational chatbots