# WisdomInterrogatory (LuWen): An Open-Source Legal Large Language Model Technical Report

arXiv: https://arxiv.org/abs/2604.06737 · HTML: https://mlsanity.com/paper/2604.06737/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 8 Apr 2026.

## Builds on (most cited first, 1 of 1)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)

## Built on by (most cited first, 1 of 1)

- [PoliLegalLM: A Technical Report on a Large Language Model for Political and Legal Affairs](https://mlsanity.com/paper/2604.17543/) (2026)

## Abstract (arXiv)

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.