arXiv AI
Citation-Closure Retrieval and Per-Rule Attribution for Real-World Regulatory Compliance Question Answering
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arXiv AI
Jun 16
Beyond Case Law: Evaluating Structure-Aware Retrieval and Safety in Statute-Centric Legal QA
arXiv:2604. 06173v2 Announce Type: replace-cross Abstract: Legal QA benchmarks have predominantly focused on case law, overlooking the unique challenges of statute-centric regulatory reasoning.
arXiv AI
Aug 18
Bye-bye, Bluebook? Automating Legal Drudgery With AI-Augmented Rule Following
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 calling for much discretion.
arXiv AI
Jul 17
L-MARS: Legal Multi-Agent System with Agentic Search and Citation-Faithfulness Audit
arXiv:2509. 00761v4 Announce Type: replace Abstract: Large language models are increasingly deployed for legal question answering, where evaluations typically focus on multiple-choice accuracy.
Hugging Face Trending Papers
Jul 23
REFACT: Adaptive Fact Restatement for Compact and Faithful Chain-of-Thought Reasoning
Large language models increasingly rely on long-form reasoning for complex tasks, yet their reasoning traces may drift away from the supplied context when evidence is sparse, noisy, or in conflict with parametric knowledge. Existing grounding methods either attach citations after generation or encourage evidence retrieval inside the trace, but they often do not ensure that cited content is sufficient for the local inference and final answer.
arXiv Computation and Language
1d ago
LegalPincite: Multi-level Legal Information Retrieval Dataset
arXiv:2608.03756v2 Announce Type: cross Abstract: A common task in legal Information Retrieval (IR) is to find relevant legal sources from case-law collections. While legal practice often requires pi...
arXiv AI
Aug 11
Temporal Misgrounding in Legal RAG: A Versioned-Corpus Benchmark for French Tax Law
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 applicable version is an earlier or future one.