SimuCourt: Building Judicial Decision-Making Agents with Real-world
  Judgement Documents
Zhitao He Pengfei Cao Chenhao Wang Zhuoran Jin Yubo Chen Jiexin Xu Huaijun Li Xiaojian Jiang Kang Liu Jun Zhao
With the development of deep learning, natural language processing technology
has effectively improved the efficiency of various aspects of the traditional
judicial industry. However, most current efforts focus solely on individual
judicial stage, overlooking cross-stage collaboration. As the autonomous agents
powered by large language models are becoming increasingly smart and able to
make complex decisions in real-world settings, offering new insights for
judicial intelligence. In this paper, (1) we introduce SimuCourt, a judicial
benchmark that encompasses 420 judgment documents from real-world, spanning the
three most common types of judicial cases, and a novel task Judicial
Decision-Making to evaluate the judicial analysis and decision-making power of
agents. To support this task, we construct a large-scale judicial knowledge
base, JudicialKB, with multiple legal knowledge. (2) we propose a novel
multi-agent framework, AgentsCourt. Our framework follows the real-world
classic court trial process, consisting of court debate simulation, legal
information retrieval and judgement refinement to simulate the decision-making
of judge. (3) we perform extensive experiments, the results demonstrate that,
our framework outperforms the existing advanced methods in various aspects,
especially in generating legal grounds, where our model achieves significant
improvements of 8.6% and 9.1% F1 score in the first and second instance
settings, respectively.