The paper introduces the first English‑Mizo parallel dataset of Indian court judgments, comprising 13,168 sentence pairs from the Gauhati High Court and the Supreme Court of India. Professional native Mizo translators produced complete documents, after which sentence pairs were manually aligned and independently reviewed. Analysis reveals selective borrowing of English words—official and legal terms remain in English while everyday terms are rendered in Mizo—and inconsistent use of Mizo diacritics. Fine‑tuning the NLLB‑200 1.3B model on this corpus yields a 27.47 BLEU score on a balanced legal test set, improving by 7.16 BLEU over the base model, yet the model still over‑uses English words compared to human translation.
arXiv:2606. 23375v2 Announce Type: replace-cross Abstract: While the wider applicability of LLMs in the legal field is currently debated due to their reliability and the gravity of any errors, narrow uses with well-understood and mitigated risks have emerged.
arXiv:2608.28645v1 Announce Type: cross Abstract: Low-resource languages without an adequate training corpus often use a related, higher-resource language as a scaffold for comprehension. Still, ther...
arXiv:2610.05366v2 Announce Type: replace Abstract: \`{I}r\`{o}y\`{i}nSpeech is a 42-hour, 80-speaker Yor\`ub\'a read-speech corpus whose audio has been distributed by ELRA since 2024. This paper des...
Thunder‑DeID+ is a Korean de‑identification system that improves boundary precision, label granularity, and recall when applied to court judgments and other sensitive documents. It introduces a refined three‑tier PII taxonomy and a boundary‑aware tokenizer that augments a pretrained BPE vocabulary with N‑gram and boundary‑scoring components, enabling better alignment of token and PII span boundaries. Evaluations show the tokenizer raises the W. Strict Typed F1‑score from 0.76‑0.77 to 0.87‑0.88 across models, and fine‑tuning on 1,000 broader documents yields a 0.93 score, demonstrating effective transfer beyond court judgments.
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.
CLASE is a hybrid evaluation method for Chinese legal text that combines linguistic feature-based scores with experience-guided LLM-as-a-judge scores. It learns from contrastive pairs of authentic legal documents and their LLM-generated counterparts, enabling transparent, reference-free assessment of stylistic quality. Experiments on 200 Chinese legal documents show that CLASE aligns better with human judgments than traditional metrics and offers interpretable score breakdowns and improvement suggestions.
arXiv:2606.09389v2 Announce Type: replace Abstract: As large language models (LLMs) are increasingly applied to real-world legal tasks, evaluating the reliability of their open-ended legal responses...
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.
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.
arXiv:2606. 18699v1 Announce Type: cross Abstract: Large language models (LLMs) have shown impressive capabilities across diverse tasks, yet their performance on jurisdiction-specific legal reasoning remains underexplored.
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. 24276v1 Announce Type: cross Abstract: Large language models (LLMs) process text through subword tokenizers rather than directly reading characters or words.