A gents C ourt: Building Judicial Decision-Making Agents with Court Debate Simulation and Legal Knowledge Augmentation (2024)

Abstract: 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 on tasks within individual judicial stages, making it difficult to handle complex tasks that span multiple stages. 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 propose a novel multi-agent framework, AgentsCourt, for judicial decision-making. Our framework follows the classic court trial process, consisting of court debate simulation, legal resources retrieval and decision-making refinement to simulate the decision-making of judge. (2) we introduce SimuCourt, a judicial benchmark that encompasses 420 Chinese judgment documents, spanning the three most common types of judicial cases. Furthermore, to support this task, we construct a large-scale legal knowledge base, Legal-KB, with multi-resource legal knowledge. (3) Extensive experiments show that our framework outperforms the existing advanced methods in various aspects, especially in generating legal articles, where our model achieves significant improvements of 8.6% and 9.1% F1 score in the first and second instance settings, respectively.

## introduction recent advances in deep learning have significantly impacted the legal domain, with notable achievements in legal question answering @xcite @xcite , legal case retrieval @xcite @xcite and legal judgment prediction @xcite @xcite . these developments have effectively alleviated the long-figure 1 : we formulate the judicial decision-making task using the real-world judgement documents: given the case details above, judge agent must 1) conduct a logically clear case analysis; 2) provide precise legal articles; 3) issue a definitive judgement. ## the agentscourt framework we propose a novel multi-agent framework, as shown in figure 3 . our framework is based on real-world court trial process and aims to study the collaboration of multiple agents, as well as how they contribute to judicial decision-making. ## the simucourt benchmark the task, judicial decision-making, requires agents to conduct case analysis, generate legal articles and judgments. however, most existing legal datasets suffer from several limitations when it comes to assessing the agent-as-judge paradigm: 1) only contain the factual information of cases; 2) only focus on criminal cases; 3) only evaluate judgments. to this end, we propose simucourt, a judicial benchmark for a reliable assessment of the judicial analysis and decision-making power of agents. a comparison between our dataset and previous works is presented in ## conclusion we propose a novel multi-agent framework agentscourt, which can sequentially simulate court debate, retrieve precedents, analyze cases, provide legal articles, and deliver clear judgment. furthermore, we introduce simucourt, a judicial benchmark to evaluate the judicial analysis and decisionmaking power of agents. then, we perform experiments to analyze different modules. the new judicial paradigm we presented effectively simulates the judicial decision-making with multi-agent, which significantly enhances judicial efficiency.