Search: “legal”

arXiv:2603.08286v2 Announce Type: replace Abstract: Legal argument mining aims to identify and classify the functional components of judicial reasoning, such as facts, issues, rules, analysis, and con

arXiv:2607.03325v1 Announce Type: cross Abstract: We present an automated pipeline that decomposes Italian tax-court judgments into individual legal issues and extracts, for each issue, a structured X

arXiv:2605.16767v1 Announce Type: new Abstract: Multi-label legal annotation requires assigning multiple labels from large, evolving taxonomies to long, fact-intensive documents, often under limited s

arXiv:2605.15380v1 Announce Type: new Abstract: Recent advances in generative AI have shown their potential to be leveraged for legal education. Yet, work on the development and deployment of such sys

arXiv:2605.10186v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into legal drafting and research workflows, where incorrect citations or fabricated precedent

arXiv:2604.19593v1 Announce Type: cross Abstract: The importance of clear and correct text in legal documents cannot be understated, and, consequently, a grammatical error correction tool meant to ass

arXiv:2609.22127v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to draft contractual language, yet conventional accuracy or preference-based evaluations are poorly m

arXiv:2609.05296v1 Announce Type: new Abstract: Does a simplified legal clause still say what the original said? The checks in current use cannot establish that it does: requiring an identical pair to

arXiv:2608.29884v1 Announce Type: new Abstract: We show that sequence-level distillation from a capable long-context teacher model is a simple, annotation-free, and data-efficient strategy for improvi

arXiv:2505.02763v2 Announce Type: replace-cross Abstract: One of the central promises of legal AI is to automate drudgery -- the formal, repetitive tasks of lawyers' work that consume time without cal

arXiv:2608.13712v1 Announce Type: cross Abstract: Deposition training requires attorneys to manage dynamic witness behavior, yet legal-AI evaluations largely focus on factual accuracy, reasoning, or r

arXiv:2606.21155v2 Announce Type: replace Abstract: Attorneys, judges, and pro se filers increasingly use AI to draft legal documents, yet these tools frequently fabricate citations. Despite predictio

arXiv:2606.28538v1 Announce Type: new Abstract: We investigate domain adaptation of modern BERT models in the legal domain. We further pre-train ModernBERT on all US court opinions using the masked la

arXiv:2606.11910v1 Announce Type: new Abstract: Traffic law liability determination is critical for assigning legal penalties, requiring the simultaneous identification of interdependent statutory pro

arXiv:2606.00272v1 Announce Type: new Abstract: The FETCH classifier generates follow-up questions to help refine the best match for the applicant's legal problem, using a low-cost ensemble of LLMs. I

arXiv:2605.21076v1 Announce Type: new Abstract: Grading German legal exam solutions faces growing volumes and a shortage of qualified graders, delaying feedback and creating a bottleneck. At the same

arXiv:2605.15362v1 Announce Type: new Abstract: Half a billion citation edges extracted from 100.7 million Ukrainian court decisions reveal that judicial citation structure encodes legal domain bounda

arXiv:2605.14665v1 Announce Type: new Abstract: Legal reasoning is not semantic similarity search. A court judgment encodes constrained symbolic reasoning: precedent propagation, procedural state tran

arXiv:2605.02318v1 Announce Type: cross Abstract: In 2011, Judea Pearl received the Turing Award, considered the Nobel Prize in Computing, for fundamental contributions to artificial intelligence thro

arXiv:2605.00977v1 Announce Type: cross Abstract: The record of the beginning of the most widespread legal system in the world is contained in millions of pages of handwritten text. Most of the record

arXiv:2605.02011v1 Announce Type: new Abstract: Automating the drafting of judgment documents is pivotal to judicial efficiency, yet it remains challenging due to the dual requirements of comprehensiv

arXiv:2604.22292v1 Announce Type: cross Abstract: The classification of legal documents from an unstructured data corpus has several crucial applications in downstream tasks. Documents relevant to cou

arXiv:2604.18878v1 Announce Type: new Abstract: We introduce LegalBench-BR, the first public benchmark for evaluating language models on Brazilian legal text classification. The dataset comprises 3,10

arXiv:2610.10971v1 Announce Type: cross Abstract: Large language models are increasingly used in legal research and drafting, but they can still produce claims that sound convincing without being supp

arXiv:2610.06439v1 Announce Type: new Abstract: What happens when a legal AI model learns to look like a lawyer instead of reasoning like one? We fine tune Qwen3-8B with Group Relative Policy Optimisa

arXiv:2601.04094v4 Announce Type: cross Abstract: Effective regulation of AI is a defining policy challenge, driven by their integration into all aspects of society. To remain responsive to their rapi

arXiv:2609.27014v1 Announce Type: cross Abstract: Legal contracts are structurally complex documents in which contradictions may emerge across distant and interconnected provisions. Although large lan

arXiv:2609.22209v1 Announce Type: new Abstract: Empirical legal research often relies on turning research questions into structured data extracted from large collections of rulings and judgments. Desi

arXiv:2609.03597v1 Announce Type: new Abstract: Land ownership in Bangladesh is recorded in Ana-Ganda-Kora-Kranti-Til, a base-16 positional fraction system with dedicated Unicode glyphs, no mainstream

arXiv:2608.29616v1 Announce Type: new Abstract: Criminal judgment prediction requires models to infer statutory articles, charges, and sentencing outcomes from case facts. Unlike standard classificati

arXiv:2608.24946v1 Announce Type: new Abstract: Macros constitute a large part of the core area in modern very large-scale integration (VLSI) designs. Moreover, macro positions have a significant impa

arXiv:2608.09393v1 Announce Type: cross Abstract: We identify and quantify temporal misgrounding: the systematic retrieval and citation of the currently in-force version of a legal article when the ap

arXiv:2606.07613v1 Announce Type: cross Abstract: Visual evidence has long been treated as a reliable form of legal proof, but advances in artificial intelligence (AI) are undermining that assumption.

arXiv:2606.00898v1 Announce Type: new Abstract: Large language models systematically hallucinate legal citations -- fabricating statute references, citing repealed provisions, and confusing jurisdicti

arXiv:2605.28183v1 Announce Type: cross Abstract: We introduce the BenGER (Benchmark for German Law) dataset for evaluating LLM systems on subsumption-based legal reasoning in German law. The BenGER d

arXiv:2605.27331v1 Announce Type: new Abstract: Competition law experts conducting legal research must review extensive volumes of cases, decisions, and judicial reports to identify precedents and ass

arXiv:2604.17674v1 Announce Type: new Abstract: Legal practitioners and judicial institutions face an ever-growing volume of case-law documents characterised by formalised language, lengthy sentence s

arXiv:2604.13583v1 Announce Type: new Abstract: Evaluating large language models (LLMs) for legal reasoning requires workflows that span task design, expert annotation, model execution, and metric-bas

arXiv:2604.06173v1 Announce Type: cross Abstract: Legal QA benchmarks have predominantly focused on case law, overlooking the unique challenges of statute-centric regulatory reasoning. In statutory do

arXiv:2610.13695v1 Announce Type: new Abstract: Any automation within reliability-critical contexts such as medicine or law has to be transparent and verifiable. Despite their advantages, current LLM-

arXiv:2609.25441v1 Announce Type: new Abstract: Legal argument mining supports passage classification, retrieval, and argument completion. This work introduces an expert-annotated dataset of 42 U.S. f

arXiv:2609.19153v1 Announce Type: new Abstract: Empirical legal scholarship increasingly treats judicial text as data, and much of it still runs on sparse, interpretable pipelines -- TF-IDF features a

arXiv:2609.06769v1 Announce Type: cross Abstract: As people increasingly rely on artificial intelligence (AI) for guidance in their own lives, scholars, lawyers, and even judges have begun to consider

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 a

arXiv:2608.21409v1 Announce Type: cross Abstract: In medicine, claims remain valid when supported by empirical evidence grounded in stable biological reality. In law, by contrast, truth is contingent,

arXiv:2608.20204v1 Announce Type: new Abstract: Legal work, with its heavy reliance on processing large amounts of text, is often considered one of the domains most exposed to the use of LLMs. Contrac