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
arXiv:2605. 29738v2 Announce Type: replace-cross Abstract: Legal NLP benchmarks overwhelmingly evaluate a single language or aggregate tasks that differ fundamentally across jurisdictions, making cross-lingual comparison impossible.
By Volodymyr Ovcharov
arXiv:2604. 04790v2 Announce Type: replace-cross Abstract: Natural language processing (NLP) advances have powered a generation of LegalTech systems, but Turkish law remains under-served by domain-specific data and models.
By Mehmet Utku \"Ozt\"urk, Tansu T\"urko\u{g}lu, Buse Buz-Yalug
The paper presents a smartphone‑compatible, retrieval‑augmented language model tailored to Bangladeshi statutory law. By distilling a 9‑billion‑parameter Gemma‑2 teacher into a 2‑billion‑parameter student using supervised fine‑tuning and QLoRA, the authors achieve significant gains in ROUGE‑L and BERTScore on an English benchmark while keeping the model lightweight (1.6 GB) and operable offline on a Pixel 6. The system retrieves from 36,029 statutory passages using a hybrid dense/BM25 approach, and cross‑lingual evaluation shows effective Bangla query handling against an English‑only corpus, with a practicing lawyer rating the responses highly in a pilot study.
By MD. Nafis Kamal, Mahadi Hasan Fahim, Talha Ridwan, Nadifa Zaman, Fariha Roushon Florin, Farig Yousuf Sadeque, Saadat Rafid Ahmed
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
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
arXiv:2608.30092v1 Announce Type: cross
Abstract: We present Arkios, a 1.04B-parameter dense transformer pretrained from scratch on 150B tokens of bilingual English-Nepali text, using a custom single...
By Sajal Regmi, Siddhartha Pudasaini, Chetan Phakami Pun
The paper compares generative and encoder-based neural models for multilingual Named Entity Recognition (NER) across the eleven languages of the Naamapadam benchmark. Five classic model families, four decoder-only large language models fine‑tuned with LoRA and 4‑bit NF4 quantisation, and nine generative models in zero‑to‑5‑shot inference were evaluated under strict CoNLL span‑level metrics. Encoder-based models (mBERT and XLM‑R) achieved substantially higher F1 scores—up to 0.675 on Hindi—than any generative architecture, with gaps of 7.5–40 percentage points; the best few‑shot result reached only 28% of the encoder baseline. The study identifies three language clusters (encoder‑dominant, partial‑coverage, and failure‑zone) and offers deployment guidelines based on transfer learning and low‑resource NLP principles.
By Jakkala Mahesh, Jatavath Shravan Kumar, Komalla Shivani, Sujoy Sarkar
arXiv:2609.22529v1 Announce Type: new
Abstract: International law provides the normative framework through which states coordinate action, regulate armed conflict, and protect human rights, yet its t...
By Genis Skura, Roland Bouffanais, Didier Wernli
arXiv:2606. 20089v1 Announce Type: cross Abstract: Persian pretrained language models (PLMs) are still limited by the scarcity of large-scale, high-quality pretraining corpora and by insufficient evaluation beyond standard classification and NER tasks.
By Arash Ghafouri, Mahdi Firouzmandi, Hossein Saberi, Mohammad Reza Hasani Ahangar
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
arXiv:2607. 19181v1 Announce Type: cross Abstract: Neural machine translation (NMT) in the legal domain is a linguistically and conceptually demanding task, primarily due to the complexity of legal language and the high level of precision it requires.
By Aixiu An, Michael Jungo, Eloi Eynard, Mark Drenhaus, Andreas Fischer, Jean Hennebert, S\'ebastien Rumley