LegalWorld is a life-cycle interactive environment modeling Chinese civil litigation as five causally connected stages grounded in 75,309 judgments, paired with LongJud-Bench for cross-stage agent evaluation.
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Disc-lawllm: Fine-tuning large language models for intelligent legal services
14 Pith papers cite this work. Polarity classification is still indexing.
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TaxPraBen is a new benchmark with 14 datasets and a structured evaluation method for measuring LLM performance on Chinese real-world tax tasks and scenarios.
VLegal-Bench supplies 10,450 expert-validated samples for evaluating LLMs on Vietnamese legal questions, retrieval, multi-step reasoning, and scenario solving.
LexRel introduces a hierarchical legal relation schema and expert benchmark for Chinese civil cases, exposing LLM limitations and downstream gains from explicit relation knowledge.
The work introduces a should-change/should-not-change evaluation suite for legal LLMs and the LexGuard adversarial framework that uses SMT solvers to enforce legal consistency.
RCBSF models contract revision as a bilevel Stackelberg game between a Global Prescriptive Agent and constrained revision/verification agents, claiming convergence to superior equilibria and 84.21% risk resolution.
LLM agents iteratively generate and optimize data processing strategies for fine-tuning, delivering over 80% win rates versus unprocessed data and 65% versus LLM-based AutoML baselines while cutting search time by up to 10x.
OMAGR decomposes queries into ontology-aligned anchors for parallel multi-dimensional graph retrieval, outperforming baselines on Context Precision and Faithfulness in the new TrafficLaw-QA dataset of 200 questions.
LegalDrill uses diagnosis-driven synthesis and self-reflective verification to create high-quality training data that improves small language models' legal reasoning without expert annotations.
PDF-WuKong adds a sparse sampler to an MLLM for efficient long-PDF multimodal QA and reports an 8.6% F1 gain over proprietary models on a new 1.1M-pair academic-paper dataset.
A survey classifying RAG foundations for AIGC, summarizing enhancements, cross-modal applications, benchmarks, limitations, and future directions.
LegalGraphRAG adds hierarchical organization to legal knowledge graphs and a multi-agent verification loop to reach claimed state-of-the-art accuracy and trustworthiness on legal reasoning benchmarks.
A survey that organizes LLMs-as-judges research into functionality, methodology, applications, meta-evaluation, and limitations.
A comprehensive survey of knowledge distillation for LLMs structured around algorithms, skill enhancement, and vertical applications, highlighting data augmentation as a key enabler.
citing papers explorer
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LegalWorld: A Life-Cycle Interactive Environment for Legal Agents
LegalWorld is a life-cycle interactive environment modeling Chinese civil litigation as five causally connected stages grounded in 75,309 judgments, paired with LongJud-Bench for cross-stage agent evaluation.
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TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice
TaxPraBen is a new benchmark with 14 datasets and a structured evaluation method for measuring LLM performance on Chinese real-world tax tasks and scenarios.
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VLegal-Bench: Cognitively Grounded Benchmark for Vietnamese Legal Reasoning of Large Language Models
VLegal-Bench supplies 10,450 expert-validated samples for evaluating LLMs on Vietnamese legal questions, retrieval, multi-step reasoning, and scenario solving.
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LexRel: Benchmarking Legal Relation Extraction for Chinese Civil Cases
LexRel introduces a hierarchical legal relation schema and expert benchmark for Chinese civil cases, exposing LLM limitations and downstream gains from explicit relation knowledge.
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Which Changes Matter? Towards Trustworthy Legal AI via Relevance-Sensitive Evaluation and Solver-Grounded Reasoning
The work introduces a should-change/should-not-change evaluation suite for legal LLMs and the LexGuard adversarial framework that uses SMT solvers to enforce legal consistency.
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RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game
RCBSF models contract revision as a bilevel Stackelberg game between a Global Prescriptive Agent and constrained revision/verification agents, claiming convergence to superior equilibria and 84.21% risk resolution.
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LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning
LLM agents iteratively generate and optimize data processing strategies for fine-tuning, delivering over 80% win rates versus unprocessed data and 65% versus LLM-based AutoML baselines while cutting search time by up to 10x.
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An Ontology-Guided Multi-Anchor Graph Retrieval Framework for Traffic Legal Liability Determination
OMAGR decomposes queries into ontology-aligned anchors for parallel multi-dimensional graph retrieval, outperforming baselines on Context Precision and Faithfulness in the new TrafficLaw-QA dataset of 200 questions.
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LegalDrill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models
LegalDrill uses diagnosis-driven synthesis and self-reflective verification to create high-quality training data that improves small language models' legal reasoning without expert annotations.
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PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling
PDF-WuKong adds a sparse sampler to an MLLM for efficient long-PDF multimodal QA and reports an 8.6% F1 gain over proprietary models on a new 1.1M-pair academic-paper dataset.
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Retrieval-Augmented Generation for AI-Generated Content: A Survey
A survey classifying RAG foundations for AIGC, summarizing enhancements, cross-modal applications, benchmarks, limitations, and future directions.
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LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning
LegalGraphRAG adds hierarchical organization to legal knowledge graphs and a multi-agent verification loop to reach claimed state-of-the-art accuracy and trustworthiness on legal reasoning benchmarks.
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LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods
A survey that organizes LLMs-as-judges research into functionality, methodology, applications, meta-evaluation, and limitations.
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A Survey on Knowledge Distillation of Large Language Models
A comprehensive survey of knowledge distillation for LLMs structured around algorithms, skill enhancement, and vertical applications, highlighting data augmentation as a key enabler.