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
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:2607. 18825v1 Announce Type: cross Abstract: This comprehensive study introduces an advanced Artificial Intelligence for Indian Legal Question Answering (AILQA) system tailored to the Indian legal context.
By Shubham Kumar Nigam, Shubham Kumar Mishra, Noel Shallum, Kripabandhu Ghosh, Arnab Bhattacharya
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...
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
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
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
The paper introduces a Nepali Question‑Answer dataset focused on passport‑related FAQs to support information retrieval in a low‑resource language. The authors fine‑tune transformer‑based embedding models for semantic similarity and compare them against the BM25 baseline. Their experiments show that fine‑tuned SBERT models outperform BM25, while multilingual E5 embeddings achieve the best overall retrieval performance.
By Funghang Limbu Begha, Praveen Acharya, Bal Krishna Bal
The paper presents a cross‑lingual legal QA system for Vietnamese labour law, introducing a bilingual evaluation suite of 231 Vietnamese–English question–answer pairs, 75 of which are annotated for five complex legal reasoning phenomena. It evaluates a verifier‑guided pipeline that decomposes answers into claims, checks citation reachability and entailment, and corrects citation failures and contradictions, and introduces six automatic diagnostics for faithfulness to retrieved evidence. Experiments show that dense retrieval outperforms sparse and hybrid retrieval, translation placement has no significant effect on diagnostics, and verifier‑guided correction modestly improves citation preservation but not other dimensions, with human evaluation indicating a gap between automatic diagnostics and human judgments.
By Nguyen Minh Chi, Mo El-Haj, Nguyen Ha Thanh, Dawn Knight, Paul Rayson
The paper surveys how large language models (LLMs) are being applied in legal tasks such as judgement prediction, document analysis, and drafting. It reviews the benefits of automation while highlighting legal challenges like privacy, bias, and explainability. The authors also discuss data resources for legal domain specialization and outline future research directions.
By Zhongxiang Sun
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: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