arXiv AI August 10, 2026 By Volodymyr Ovcharov

Multi-Legal-Bench: Evaluating LLMs on Legal Reasoning Across Jurisdictions, Languages, and Legal Traditions

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Sep 23

IntLawNER: A Named Entity Recognition Dataset and Benchmark in International Law

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

llms agents nlp fine-tuning benchmarks

arXiv AI

Sep 15

Building Legal Reward Models for Grounding and Abstention

arXiv:2609.14739v1 Announce Type: cross Abstract: Large language models are increasingly used in high-stakes domains such as law, where systems must ground their reasoning in retrieved evidence and a...

By Rilton Franzone, Valentin No\"el, Puyu Wang, Philip Torr, Fabio J. Fehr

llms rag reinforcement-learning benchmarks

arXiv AI

Aug 17

Fine-grained Claim-level RAG Benchmark for Law

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

llms rag benchmarks safety

arXiv AI

Jul 22

Reasoning Before Translation: Enhancing Legal Machine Translation with Structured Reasoning

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

llms nlp reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 18

TW-LegalBench: Measuring Taiwanese Legal Understanding

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.

By Fei-Yueh Chen, Chun Huang Lin, Chan Wei Hsu, Kuan Hsuan Yeh, Zih-Ching Chen, Kuan-Ming Chen, Patrick Chung-Chia Huang

llms benchmarks

arXiv AI

Jul 1

Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian

arXiv:2606. 31718v1 Announce Type: cross Abstract: Relation extraction (RE) for low-resource languages is typically constrained by the lack of annotated corpora.

By Dragos-Mitrut Vasile, Elena-Simona Apostol, Stefan-Adrian Toma, Adrian Paschke, Ciprian-Octavian Truica

llms fine-tuning benchmarks

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Related stories

IntLawNER: A Named Entity Recognition Dataset and Benchmark in International Law

Building Legal Reward Models for Grounding and Abstention

Fine-grained Claim-level RAG Benchmark for Law

Reasoning Before Translation: Enhancing Legal Machine Translation with Structured Reasoning

TW-LegalBench: Measuring Taiwanese Legal Understanding

Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian