In this paper, we review legal testing methods based on Large Language Models (LLMs), using the OPENAI o1 model as a case study to evaluate the performance of large models in applying legal provisions. We compare current state-of-the-art LLMs, including open-source, closed-source, and legal-specific models trained specifically for the legal domain. Systematic tests are conducted on English and Chinese legal cases, and the results are analyzed in depth. Through systematic testing of legal cases from common law systems and China, this paper explores the strengths and weaknesses of LLMs in understanding and applying legal texts, reasoning through legal issues, and predicting judgments. The experimental results highlight both the potential and limitations of LLMs in legal applications, particularly in terms of challenges related to the interpretation of legal language and the accuracy of legal reasoning. Finally, the paper provides a comprehensive analysis of the advantages and disadvantages of various types of models, offering valuable insights and references for the future application of AI in the legal field.

翻译：本文综述了基于大型语言模型（LLM）的法律测试方法，以OPENAI o1模型为案例，评估大模型在适用法律条文方面的性能。我们比较了当前最先进的LLM，包括开源模型、闭源模型以及专门针对法律领域训练的法律专用模型。对英文和中文法律案例进行了系统性测试，并对结果进行了深入分析。通过对普通法系和中国法律案例的系统性测试，本文探讨了LLM在理解与适用法律文本、法律问题推理以及判决预测方面的优势与不足。实验结果凸显了LLM在法律应用中的潜力与局限，特别是在法律语言解释和法律推理准确性方面面临的挑战。最后，本文全面分析了各类模型的优缺点，为人工智能在法律领域的未来应用提供了有价值的见解与参考。