The paper presents a knowledge graph built from Harvard Dataverse’s public data, linking 102,650 datasets to 215,985 nodes and 528,003 edges that include keywords, publications, subjects, journals, and locations. About 43,991 datasets contain geospatial metadata, and 7,654 are identified as policy‑relevant, with elections and legislatures forming the largest cluster. The authors highlight the challenge of place resolution—disconnected nodes representing the same location—and propose the graph as a testbed for AI‑driven metadata enrichment and entity resolution, noting a bias toward American city‑level data.
By Danny EBanks, Devika Jain
The paper introduces Legal Rule Induction (LRI), a task that seeks to extract concise, generalizable doctrinal rules from analogous judicial precedents. It presents a reproducible pipeline for constructing LRI datasets and, using Chinese law, releases the first benchmark comprising 5,121 case sets (38,088 court cases) for training and 216 expert‑annotated gold test sets. Experiments show that state‑of‑the‑art large language models struggle with over‑generalization and hallucination, but training on the new dataset significantly improves their ability to capture nuanced rule patterns across similar cases.
By Wei Fan, Tianshi Zheng, Yiran Hu, Zheye Deng, Weiqi Wang, Baixuan Xu, Chunyang Li, Haoran Li, Weixing Shen, Yangqiu Song
Legal Research Bench (LRB) is a new benchmark comprising 413 open-ended U.S. legal research questions, each paired with a gold answer, supporting authorities, and a binary grading rubric. The study evaluates thirteen advanced language‑model agents using web search, case‑law search, page parsing, and retrieval tools, scoring responses only when all required criteria are met and cited authorities verify. Results show that even the best model, Claude Opus 4.8, achieves full correctness on only 42.9% of questions, with performance varying by legal area and task complexity, and no clear link between more tool calls or inference cost and higher accuracy.
By Katrina Drozdov, Oliver Chen, Langston Nashold, Rayan Krishnan
arXiv:2509.00141v2 Announce Type: replace-cross
Abstract: Statutory corpora and judicial decisions are growing faster than legal professionals can read them, while individual judgments often exceed t...
By Anuraj Maurya
arXiv:2608.28593v1 Announce Type: new
Abstract: With the increasing development of AI regulatory frameworks, ensuring that artificial intelligence systems, particularly generative models, operate in...
By Cindy Delage, St\'ephane Canu, Marc D\'ecombas, Jonathan Foureur
arXiv:2608.20391v1 Announce Type: new
Abstract: Most legal NLP resources draw from federal case law and focus on coarse classification, leaving administrative adjudication, where the vast majority of...
By Amirhossein Afsharrad, Seyed Shahabeddin Mousavi
GreenLeaf Law Embed Tiny is a 0.6 B parameter embedding model designed for legal domain retrieval. It achieves 75.11 % on the Massive Legal Embedding Benchmark and 64.38 % on MTEB(Law, v1), outperforming other models under 1 B parameters. The model is trained via a two‑stage pipeline that distills knowledge from a larger teacher, fine‑tunes with hard negative mining, and uses a curated dataset of 3.4 million query‑passage pairs, including 150,000 human‑curated samples from diverse legal jurisdictions, while supporting efficient inference with multiple quantization levels.
By Surya Saka
arXiv:2605.29742v2 Announce Type: replace
Abstract: Deploying Large Language Models (LLMs) for regulatory compliance demands rigorous traceability via comprehensive citations across multi-tiered auth...
By Yeong-Joon Ju, Seong-Whan Lee
The paper critiques the common practice of stopword removal in legal text analysis, showing that standard stoplists actually degrade performance on binary classification tasks involving Supreme Court opinions. By exhaustively testing the removal of each of ~18,500 candidate words, the authors find that no stoplist—generic or optimized—outperforms a no‑removal baseline, and that models cannot predict which words are beneficial to remove. The study argues that inherited preprocessing defaults can distort the doctrinal and ideological signals that legal scholars aim to recover, calling into question the validity of such practices.
By Gregory M. Dickinson
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
arXiv:2506.01732v4 Announce Type: replace
Abstract: Large Language Models (LLMs) are pre-trained on large amounts of data from different sources and domains. Such datasets often contain trillions of...
By Pierre-Carl Langlais, Pavel Chizhov, Catherine Arnett, Carlos Rosas-Hinostroza, Mattia Nee, Eliot Krzystof Jones, Ir\`ene Girard, David Mach, Anastasia Stasenko, Ivan P. Yamshchikov
As agents grow more capable, legal-domain LLM agents promise to turn document-heavy matters into reviewable work products -- yet reliable deployment faces three obstacles: no large-scale evidence on how today's strongest model-and-harness combinations behave on end-to-end legal matters; no agent architecture adapted to the legal vertical, only general-purpose harnesses; and, in a setting that keeps shifting with new facts, authorities, and deadlines, no mechanism for systems to learn from their own outcomes. We address each.