LexPro-1.0 Technical Report

• URL: http://arxiv.org/abs/2503.06949v2
• Date: Tue, 11 Mar 2025 04:58:27 GMT
• Title: LexPro-1.0 Technical Report
• Authors: Haotian Chen, Yanyu Xu, Boyan Wang, Chaoyue Zhao, Xiaoyu Han, Fang Wang, Lizhen Cui, Yonghui Xu,
• Abstract summary: We introduce our first-generation reasoning model, LexPro-1.0, a large language model designed for the highly specialized Chinese legal domain.<n>To address this, we first compile millions of legal documents covering over 20 types of crimes from 31 provinces in China for model training.<n>The model further undergoes large-scale reinforcement learning without additional supervision, emphasizing the enhancement of its reasoning capabilities and explainability.
• Score: 19.83460019437367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this report, we introduce our first-generation reasoning model, LexPro-1.0, a large language model designed for the highly specialized Chinese legal domain, offering comprehensive capabilities to meet diverse realistic needs. Existing legal LLMs face two primary challenges. Firstly, their design and evaluation are predominantly driven by computer science perspectives, leading to insufficient incorporation of legal expertise and logic, which is crucial for high-precision legal applications, such as handling complex prosecutorial tasks. Secondly, these models often underperform due to a lack of comprehensive training data from the legal domain, limiting their ability to effectively address real-world legal scenarios. To address this, we first compile millions of legal documents covering over 20 types of crimes from 31 provinces in China for model training. From the extensive dataset, we further select high-quality for supervised fine-tuning, ensuring enhanced relevance and precision. The model further undergoes large-scale reinforcement learning without additional supervision, emphasizing the enhancement of its reasoning capabilities and explainability. To validate its effectiveness in complex legal applications, we also conduct human evaluations with legal experts. We develop fine-tuned models based on DeepSeek-R1-Distilled versions, available in three dense configurations: 14B, 32B, and 70B.

Related papers

• Evaluating Test-Time Scaling LLMs for Legal Reasoning: OpenAI o1, DeepSeek-R1, and Beyond [29.03425022434831]
  Test-Time Scaling Large Language Models (LLMs) have demonstrated exceptional capabilities across various domains and tasks, particularly in reasoning. We present a preliminary evaluation of LLMs in various legal scenarios, covering both Chinese and English legal tasks. Our findings indicate that, despite DeepSeek-R1 and OpenAI o1 being among the most powerful models, their legal reasoning capabilities are still lacking.
  arXiv  Detail & Related papers  (2025-03-20T11:14:39Z)
• AnnoCaseLaw: A Richly-Annotated Dataset For Benchmarking Explainable Legal Judgment Prediction [56.797874973414636]
  AnnoCaseLaw is a first-of-its-kind dataset of 471 meticulously annotated U.S. Appeals Court negligence cases.<n>Our dataset lays the groundwork for more human-aligned, explainable Legal Judgment Prediction models.<n>Results demonstrate that LJP remains a formidable task, with application of legal precedent proving particularly difficult.
  arXiv  Detail & Related papers  (2025-02-28T19:14:48Z)
• LegalAgentBench: Evaluating LLM Agents in Legal Domain [53.70993264644004]
  LegalAgentBench is a benchmark specifically designed to evaluate LLM Agents in the Chinese legal domain.<n>LegalAgentBench includes 17 corpora from real-world legal scenarios and provides 37 tools for interacting with external knowledge.
  arXiv  Detail & Related papers  (2024-12-23T04:02:46Z)
• NyayaAnumana & INLegalLlama: The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis [5.790242888372048]
  This paper introduces NyayaAnumana, the largest and most diverse corpus of Indian legal cases compiled for legal judgment prediction (LJP)<n>NyayaAnumana includes a wide range of cases from the Supreme Court, High Courts, Tribunal Courts, District Courts, and Daily Orders.<n>In addition to the dataset, we present INLegalLlama, a domain-specific generative large language model (LLM) tailored to the intricacies of the Indian legal system.
  arXiv  Detail & Related papers  (2024-12-11T13:50:17Z)
• LexEval: A Comprehensive Chinese Legal Benchmark for Evaluating Large Language Models [17.90483181611453]
  Large language models (LLMs) have made significant progress in natural language processing tasks and demonstrate considerable potential in the legal domain. Applying existing LLMs to legal systems without careful evaluation of their potential and limitations could pose significant risks in legal practice. We introduce a standardized comprehensive Chinese legal benchmark LexEval.
  arXiv  Detail & Related papers  (2024-09-30T13:44:00Z)
• LawLLM: Law Large Language Model for the US Legal System [43.13850456765944]
  We introduce the Law Large Language Model (LawLLM), a multi-task model specifically designed for the US legal domain. LawLLM excels at Similar Case Retrieval (SCR), Precedent Case Recommendation (PCR), and Legal Judgment Prediction (LJP) We propose customized data preprocessing techniques for each task that transform raw legal data into a trainable format.
  arXiv  Detail & Related papers  (2024-07-27T21:51:30Z)
• InternLM-Law: An Open Source Chinese Legal Large Language Model [72.2589401309848]
  InternLM-Law is a specialized LLM tailored for addressing diverse legal queries related to Chinese laws. We meticulously construct a dataset in the Chinese legal domain, encompassing over 1 million queries. InternLM-Law achieves the highest average performance on LawBench, outperforming state-of-the-art models, including GPT-4, on 13 out of 20 subtasks.
  arXiv  Detail & Related papers  (2024-06-21T06:19:03Z)
• Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration [52.57055162778548]
  Legal Judgment Prediction (LJP) has become an increasingly crucial task in Legal AI. Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems. Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task.
  arXiv  Detail & Related papers  (2023-10-13T16:47:20Z)
• Chatlaw: A Multi-Agent Collaborative Legal Assistant with Knowledge Graph Enhanced Mixture-of-Experts Large Language Model [30.30848216845138]
  Chatlaw is an innovative legal assistant utilizing a Mixture-of-Experts (MoE) model and a multi-agent system. By integrating knowledge graphs with artificial screening, we construct a high-quality legal dataset to train the MoE model. Our MoE model outperforms GPT-4 in the Lawbench and Unified Exam Qualification for Legal Professionals by 7.73% in accuracy and 11 points, respectively.
  arXiv  Detail & Related papers  (2023-06-28T10:48:34Z)
• SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval [75.05173891207214]
  Legal case retrieval plays a core role in the intelligent legal system. Most existing language models have difficulty understanding the long-distance dependencies between different structures. We propose a new Structure-Aware pre-traIned language model for LEgal case Retrieval.
  arXiv  Detail & Related papers  (2023-04-22T10:47:01Z)
• Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents [56.40163943394202]
  We release the Longformer-based pre-trained language model, named as Lawformer, for Chinese legal long documents understanding. We evaluate Lawformer on a variety of LegalAI tasks, including judgment prediction, similar case retrieval, legal reading comprehension, and legal question answering.
  arXiv  Detail & Related papers  (2021-05-09T09:39:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.