PoliLegalLM: A Technical Report on a Large Language Model for Political and Legal Affairs
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 corp...
| Authors | Yuting Huang, Yinghao Hu, Qian Xiao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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