This comprehensive study introduces an advanced Artificial Intelligence for Indian Legal Question Answering (AILQA) system tailored to the Indian legal context. AILQA leverages a variety of embedding and generative models, including recent Large Language Models (LLMs), to address the unique challenges posed by the intricate and diverse nature of Indian legal texts and to enhance the accuracy and reliability of responses to legal questions.
arXiv:2606. 07523v1 Announce Type: cross Abstract: Legal domains in high-resource languages like English have widely adopted artificial intelligence for legal question answering.
By Samir Wagle, Abiral Adhikari, Reewaj Khanal, Batsal Bhandari, Prashant Manandhar, Praveen Acharya, Bal Krishna Bal
The paper introduces NepKANUN, an AI-powered legal assistant designed specifically for Nepali legal texts. Built on a fine‑tuned large language model and integrated into a Retrieval‑Augmented Generation (RAG) framework, it delivers precise answers to natural language legal queries. Evaluated with BERTScore, the system achieved strong F1 scores of 0.82, 0.77, and 0.71 across simple, moderate, and complex question categories, and expert reviews confirm its usability.
By Bhabuk Thapa, Prasiddha Koirala, Ranjit Raut, Sunil Regmi, Bal Krishna Bal
ViLegalExpert is a large-scale Vietnamese legal benchmark built from real citizen–lawyer consultations, comprising over 172,000 questions across 34 legal domains with professional answers and expert-verified evidence. It supports legal information retrieval, extractive QA, and abstractive QA. Experiments show that while pretrained language models perform well on QA, hybrid retrieval methods achieve the best evidence retrieval, highlighting significant challenges in grounding legal answers to authoritative sources.
By Dat Tien Nguyen, Nghia Hieu Nguyen, Anh Thi-Hoang Nguyen, Dung Ha Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen
arXiv:2605. 21071v4 Announce Type: replace-cross Abstract: The rapid progress of large language models (LLMs) is shifting semantic search toward a question-answering paradigm, where users ask questions and LLMs generate responses.
By Souvick Das, Sallam Abualhaija, Domenico Bianculli
arXiv:2608. 08830v1 Announce Type: new Abstract: Legal Statute Prediction (LSP) involves automatically identifying relevant legal statutes given factual descriptions in legal documents, typically framed as a multi-label classification task within natural language processing and information retrieval research.
By Subinay Adhikary, Upal Bhattacharya, Vivek Kumar Singh, Anurag Sharma, Shubham Kumar Nigam, Suvasis Das, Shouvik Kumar Guha, Koustav Rudra, Kripabandhu Ghosh
Trustworthy Legal AI requires systems that can answer legal questions while grounding their responses in authoritative sources. However, existing Vietnamese legal benchmarks provide limited coverage o...
CoAL‑RAG is a complexity‑aware legal retrieval‑augmented generation method that adapts its retrieval strategy based on a multi‑dimensional evaluation of question essence and retrieval consistency. It quantifies reasoning demand from the logical structure of a question and uses the discrepancy between semantic and keyword retrieval to gauge problem complexity, thereby selecting the most suitable retrieval approach and filtering context dynamically. Experiments show that CoAL‑RAG outperforms baseline models on Chinese legal benchmarks (SocialLawQA, LawBench) with a 42.5% BLEU improvement and 3.6× ROUGE‑L, while also achieving strong cross‑jurisdictional performance on English datasets (LexGLUE, CaseHold).
By Jin Su, Zhuofeng Zhao, Huanhuan Wang, Hao Chen
Legal Research Bench (LRB) is a new benchmark comprising 413 open-ended U.S. legal research questions, each paired with a gold answer, supporting authorities, and a binary grading rubric. The study evaluates thirteen advanced language‑model agents using web search, case‑law search, page parsing, and retrieval tools, scoring responses only when all required criteria are met and cited authorities verify. Results show that even the best model, Claude Opus 4.8, achieves full correctness on only 42.9% of questions, with performance varying by legal area and task complexity, and no clear link between more tool calls or inference cost and higher accuracy.
By Katrina Drozdov, Oliver Chen, Langston Nashold, Rayan Krishnan
arXiv:2505. 02763v2 Announce Type: replace-cross Abstract: One of the central promises of legal AI is to automate drudgery -- the formal, repetitive tasks of lawyers' work that consume time without calling for much discretion.
By Matthew Dahl, Eric Mart\'inez
LexIssue introduces a benchmark for identifying disputed legal issues in Chinese civil litigation, comprising 430 real‑world cases and 1,303 expert‑annotated issues. The dataset is built around a hierarchical schema that links free‑form issue descriptions to structured legal categories, enabling two complementary tasks: issue generation and issue classification. A retrieval‑augmented knowledge base covering 27 causes of action and 441 issue entries is provided, and experiments show that incorporating this knowledge consistently improves model performance on the tasks.
By Huiyuan Xie, Yuqin Huang, Zhicheng Hao, Yida Cai, Shaochun Wang, Zhenghao Liu, Yuxiao Ye
arXiv:2606. 15646v1 Announce Type: new Abstract: Large Language Models (LLMs) have transformed natural language processing, but their lack of interpretable reasoning and tendency to hallucinate pose significant challenges for legal applications.
By Deepa Tilwani, Yash Saxena, Ankur Padia, Srinivasan Parthasarathy, Manas Gaur