most citedThinking Longer, Not Always Smarter: Evaluating LLM Capabilities in Hierarchical Legal Reasoning

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cs.CL2026

DLawBench: Evaluating LLMs Through Multi-Turn Legal Consultation

Li Zhang, Yuzhen Shi, Yiran Hu +15

Lawyer-client consultation is a critical starting point for legal services. Effective legal assistance hinges on eliciting sufficient and truthful information from clients in order…

cs.CL2026

Retrieval-Based Multi-Label Legal Annotation: Extensible, Data-Efficient and Hallucination-Free

Li Zhang, Jaromir Savelka, Kevin Ashley

Multi-label legal annotation requires assigning multiple labels from large, evolving taxonomies to long, fact-intensive documents, often under limited supervision. Parametric encod…

cs.CL20261 cited

Thinking Longer, Not Always Smarter: Evaluating LLM Capabilities in Hierarchical Legal Reasoning

Li Zhang, Matthias Grabmair, Morgan Gray +1

Case-based reasoning is a cornerstone of U.S. legal practice, requiring professionals to argue about a current case by drawing analogies to and distinguishing from past precedents.…

cs.CL2025

Do LLMs Truly Understand When a Precedent Is Overruled?

Li Zhang, Jaromir Savelka, Kevin Ashley

Large language models (LLMs) with extended context windows show promise for complex legal reasoning tasks, yet their ability to understand long legal documents remains insufficient…

cs.CL2025

Measuring Faithfulness and Abstention: An Automated Pipeline for Evaluating LLM-Generated 3-ply Case-Based Legal Arguments

Li Zhang, Morgan Gray, Jaromir Savelka +1

Large Language Models (LLMs) demonstrate potential in complex legal tasks like argument generation, yet their reliability remains a concern. Building upon pilot work assessing LLM…