PoliLegalLM: A Technical Report on a Large Language Model for Political and Legal Affairs

• URL: http://arxiv.org/abs/2604.17543v1
• Date: Sun, 19 Apr 2026 17:06:56 GMT
• Title: PoliLegalLM: A Technical Report on a Large Language Model for Political and Legal Affairs
• Abstract summary: PoliLegalLM is a domain-specific large language model tailored for political and legal applications.<n>We construct a large-scale, high-quality legal corpus and design a structured post-training pipeline.<n>We evaluate PoliLegalLM on three representative benchmarks, including LawBench, LexEval, and a real-world dataset, PoliLegal.
• Score: 42.80108620217788
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have achieved remarkable success in general-domain tasks, yet their direct application to the legal domain remains challenging due to hallucinated legal citations, incomplete knowledge coverage, and weak structured reasoning. To address these issues, we propose PoliLegalLM, a domain-specific large language model tailored for political and legal applications. Our approach adopts a unified training framework that integrates continued pretraining, progressive supervised fine-tuning, and preference-based reinforcement learning to jointly enhance legal knowledge grounding, task alignment, and reasoning capability. We construct a large-scale, high-quality legal corpus and design a structured post-training pipeline, enabling the model to effectively learn domain-specific knowledge and adapt to diverse legal tasks. We evaluate PoliLegalLM on three representative benchmarks, including LawBench, LexEval, and a real-world dataset, PoliLegal. Experimental results demonstrate that PoliLegalLM achieves strong and consistent performance, outperforming competitive models of similar scale and remaining highly competitive with significantly larger models, while achieving the best results on real-world legal scenarios. These results highlight the effectiveness of our training paradigm and the practical value of domain-specific LLMs for real-world legal applications.

Related papers

• LegalMidm: Use-Case-Driven Legal Domain Specialization for Korean Large Language Model [22.5210475970878]
  We present a methodology for constructing high-quality, use-case-driven legal datasets and optimized training pipelines.<n>Our approach emphasizes collaboration with legal professionals and rigorous data curation to ensure relevance and factual accuracy.
  arXiv  Detail & Related papers  (2026-04-28T07:07:22Z)
• WisdomInterrogatory (LuWen): An Open-Source Legal Large Language Model Technical Report [55.27414605169639]
  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.
  arXiv  Detail & Related papers  (2026-04-08T06:59:07Z)
• 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)
• PLawBench: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice [67.71760070255425]
  We introduce PLawBench, a practical benchmark for evaluating large language models (LLMs) in legal practice scenarios.<n>PLawBench comprises 850 questions across 13 practical legal scenarios, with each question accompanied by expert-designed evaluation rubrics.<n>Using an LLM-based evaluator aligned with human expert judgments, we evaluate 10 state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2026-01-23T11:36:10Z)
• Universal Legal Article Prediction via Tight Collaboration between Supervised Classification Model and LLM [42.11889345473452]
  Legal Article Prediction (LAP) is a critical task in legal text classification.<n>We propose Uni-LAP, a universal framework for legal article prediction.
  arXiv  Detail & Related papers  (2025-09-26T09:42:20Z)
• ALKAFI-LLAMA3: Fine-Tuning LLMs for Precise Legal Understanding in Palestine [0.0]
  This study addresses the challenges of adapting Large Language Models to the Palestinian legal domain.<n>Political instability, fragmented legal frameworks, and limited AI resources hinder effective machine-learning applications.<n>We present a fine-tuned model based on a quantized version of Llama-3.2-1B-Instruct, trained on a synthetic data set derived from Palestinian legal texts.
  arXiv  Detail & Related papers  (2024-12-19T11:55:51Z)
• Optimizing Numerical Estimation and Operational Efficiency in the Legal Domain through Large Language Models [13.067312163677933]
  We propose a novel approach integrating Large Language Models with specially designed prompts to address precision requirements in legal Artificial Intelligence (LegalAI) applications. To validate this method, we introduce a curated dataset tailored to precision-oriented LegalAI tasks.
  arXiv  Detail & Related papers  (2024-07-26T18:46:39Z)
• 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)

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.