WisdomInterrogatory (LuWen): An Open-Source Legal Large Language Model Technical Report
- URL: http://arxiv.org/abs/2604.06737v2
- Date: Fri, 10 Apr 2026 07:09:04 GMT
- Title: WisdomInterrogatory (LuWen): An Open-Source Legal Large Language Model Technical Report
- Abstract summary: We present WisdomInterrogatory (LuWen), an open-source Chinese legal language model built upon the Baichuan foundation model.<n>We evaluate LuWen on five representative legal tasks spanning both prediction and generation settings.
- Score: 55.27414605169639
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models have demonstrated remarkable capabilities across a wide range of natural language processing tasks, yet their application in the legal domain remains challenging due to the specialized terminology, complex reasoning requirements, and rapidly evolving legal knowledge involved. In this paper, we present WisdomInterrogatory (LuWen), an open-source Chinese legal language model built upon the Baichuan foundation model through three key techniques: continual pre-training on a large-scale legal corpus, supervised fine-tuning with carefully curated legal instruction data, and retrieval-augmented generation integrated with a comprehensive legal knowledge base. We evaluate LuWen on five representative legal tasks spanning both prediction and generation settings, including legal judgment prediction, judicial examination, legal text summarization, law article question answering, and judicial decision reasoning. Experimental results show that LuWen outperforms several strong baselines, demonstrating the effectiveness of our approach in adapting general-purpose language models to the legal domain.
Related papers
- LegalOne: A Family of Foundation Models for Reliable Legal Reasoning [54.57434222018289]
We present LegalOne, a family of foundational models specifically tailored for the Chinese legal domain.<n>LegalOne is developed through a comprehensive three-phase pipeline designed to master legal reasoning.<n>We publicly release the LegalOne weights and the LegalKit evaluation framework to advance the field of Legal AI.
arXiv Detail & Related papers (2026-01-31T10:18:32Z) - ReGal: A First Look at PPO-based Legal AI for Judgment Prediction and Summarization in India [10.522785783474857]
We introduce Reinforcement Learning-based Legal Reasoning (ReGal), a framework that integrates Multi-Task Instruction Tuning with Reinforcement Learning from AI Feedback.<n>Our approach is evaluated across two critical legal tasks: (i) Court Judgment Prediction and Explanation (CJPE), and (ii) Legal Document Summarization.
arXiv Detail & Related papers (2025-12-19T19:13:41Z) - ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation [56.79698529022327]
Legal claims refer to the plaintiff's demands in a case and are essential to guiding judicial reasoning and case resolution.<n>This paper explores the problem of legal claim generation based on the given case's facts.<n>We construct ClaimGen-CN, the first dataset for Chinese legal claim generation task.
arXiv Detail & Related papers (2025-08-24T07:19:25Z) - GLARE: Agentic Reasoning for Legal Judgment Prediction [60.13483016810707]
Legal judgment prediction (LJP) has become increasingly important in the legal field.<n>Existing large language models (LLMs) have significant problems of insufficient reasoning due to a lack of legal knowledge.<n>We introduce GLARE, an agentic legal reasoning framework that dynamically acquires key legal knowledge by invoking different modules.
arXiv Detail & Related papers (2025-08-22T13:38:12Z) - Legal Evalutions and Challenges of Large Language Models [42.51294752406578]
We use the OPENAI o1 model as a case study to evaluate the performance of large models in applying legal provisions.
We compare current state-of-the-art LLMs, including open-source, closed-source, and legal-specific models trained specifically for the legal domain.
arXiv Detail & Related papers (2024-11-15T12:23:12Z) - Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges [7.767611860493713]
This survey follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, reviewing 154 studies, with a final selection of 131 after manual filtering.<n>It explores foundational concepts related to NLP in the legal domain, illustrating the unique aspects and challenges of processing legal texts.<n>We provide an overview of NLP tasks specific to legal text, such as Document Summarisation, Named Entity Recognition, Question Answering, Argument Mining, Text Classification, and Judgement Prediction.
arXiv Detail & Related papers (2024-10-25T01:17:02Z) - 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) - 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.