The paper evaluates legal text classification models for Korean sexual offense cases, comparing traditional machine learning, large language models, and fine‑tuned domain models. Fine‑tuned KLUE‑BERT achieved the highest accuracy of 99.3%, outperforming GPT‑3.5, GPT‑4.0, and other traditional approaches. Explainable AI techniques were used to analyze predictions, revealing linguistic features that influence decisions and highlighting limitations in capturing subtle textual cues, especially in real‑world KICS data.
By Jeongmin Lee
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:2609.16010v1 Announce Type: new
Abstract: The complexity of legal language and limited accessibility to legal information pose significant challenges to justice delivery in Nepal. Traditional l...
By Ranjit Raut, Tishya Dhakal, Aaryan Shakya, Bhabuk Thapa, Prasiddha Koirala, Bal Krishna Bal
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:2607. 29066v1 Announce Type: cross Abstract: Deception detection has critical implications for legal proceedings, law enforcement, and online security.
By Theekshana Samaradiwakara, Nisansa de Silva, George C. Lobb
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