# ShiZhi: A Chinese Lightweight Large Language Model for Court View Generation

arXiv: https://arxiv.org/abs/2510.09297 · HTML: https://mlsanity.com/paper/2510.09297/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning](https://mlsanity.com/paper/2408.05517/) (2024)
- [SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval](https://mlsanity.com/paper/2304.11370/) (2023)
- [Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions](https://mlsanity.com/paper/1802.08504/) (2018)
- [Large Language Models Meet Legal Artificial Intelligence: A Survey](https://mlsanity.com/paper/2509.09969/) (2025)
- [Event Grounded Criminal Court View Generation with Cooperative (Large) Language Models](https://mlsanity.com/paper/2404.07001/) (2024)
- [Enhancing Court View Generation with Knowledge Injection and Guidance](https://mlsanity.com/paper/2403.04366/) (2024)

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

- [Exploring Lightweight Large Language Models for Court View Generation](https://mlsanity.com/paper/2605.16770/) (2026)

## Abstract (arXiv)

Criminal Court View Generation (CVG) is a fundamental task in legal artificial intelligence, aiming to automatically generate the "Court View" section of a legal case document. Generating court views is challenging due to the diversity and complexity of case facts, and directly generating from raw facts may limit performance. In this paper, we present ShiZhi, the first large language model (LLM) specifically designed for court view generation. We construct a Chinese Court View Generation dataset, CCVG, of more than 110K cases, each containing fact descriptions paired with corresponding court views. Based on this dataset, ShiZhi achieving 70.00 ROUGE-1 and 67.85 BLEU-1 on court view generation, as well as 86.48% accuracy with 92.75% macro F1 on charge prediction. Experimental results demonstrate that even a small LLM can generate reasonable and legally coherent court views when trained on high-quality domain-specific data. Our model and dataset are available at hrefhttps://github.com/ZhitianHou/ShiZhihttps://github.com/ZhitianHou/ShiZhi.