Enterprise AI Analysis
LexRel: Benchmarking Legal Relation Extraction for Chinese Civil Cases
This paper introduces LexRel, a benchmark for legal relation extraction in Chinese civil law, featuring a comprehensive schema of 265 relation types. It evaluates LLMs, showing limitations but also SFT improvements, and demonstrates the utility of legal relations for downstream legal AI tasks. The schema helps AI systems distinguish and master legal relations, which are crucial for resolving disputes and legal reasoning.
0.762
Highest Micro-F1 (Type Extraction)
Key Metrics
Our analysis of 'LexRel: Benchmarking Legal Relation Extraction for Chinese Civil Cases' reveals critical insights into the advancements and applications of AI in legal relation extraction. These key metrics highlight the scope and potential impact.
265
Relation Types
1140
Annotated Samples
9
Major Domains
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Schema Development
Benchmarking LLMs
Downstream Impact
This section details the creation of a comprehensive hierarchical taxonomy for legal relations in Chinese civil law, covering 9 major domains and 265 relation types, with precise definitions of subjects, objects, and content. It emphasizes the expert-guided refinement process that ensures conceptual rigor and real-world applicability.
The paper defines a legal relation extraction task and introduces LexRel, an expert-annotated benchmark. It evaluates state-of-the-art LLMs using zero-shot and relation-enhanced baselines, revealing significant limitations in accurately identifying civil legal relations from case facts, especially for argument extraction.
It demonstrates that incorporating legal relations information consistently enhances performance on downstream legal AI tasks like Document Proofreading, Statute Prediction, and Consultation. This highlights the practical value of explicit legal relation modeling for knowledge-intensive legal applications.
Key Insight: Fine-Grained Legal Relations
265 Fine-Grained Legal Relation Types IdentifiedLexRel Workflow
Draft Taxonomy Construction
→
Expert-Guided Refinement
→
Task Definition
→
LLM Annotation (Drafting)
→
Expert Annotation (Review & Refine)
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LexRel Benchmark
| Model | Type Extraction (Micro-F1) | Argument Extraction (Micro-F1) |
|---|---|---|
| o3-mini (zero-shot) | 0.762 | 0.382 |
| Qwen3-14B (RE w/ DeepSeek-R1) | 0.733 | 0.381 |
| GPT-4o (zero-shot) | 0.670 | 0.224 |
Notes: Relation-enhanced (RE) baselines significantly improve performance for smaller models, demonstrating the value of SFT.
Key Insight: Impact on Legal AI Tasks
Integrating legal relations (w/ LR) consistently boosts performance across Document Proofreading, Statute Prediction, and Consultation tasks. For example, MiniCPM4-8B sees a significant jump in Document Proofreading from 5.70 to 11.78, indicating that structured legal knowledge helps models anchor analysis and reason more effectively over statutory knowledge.
Outcome: Improved Accuracy & Interpretability
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