作为税务律师的大型语言模型:法律能力出现的案例研究
John J Nay1, David Karamardian2, Sarah B Lawsky3
1CodeX, Center for Legal Informatics, Stanford University, Stanford, CA, USA.
概括
大型语言模型 (LLM) 显示了税法分析的新兴能力,在OpenAI模型发布中性能得到改善. 诸如提供法律上下文和少数镜头提示等改进显著提高了准确性,尽管专家水平尚未达到.
科学领域:
- 法律技术 法律技术
- 人工智能在法律中的作用
- 计算法 计算法 计算法
背景情况:
- 了解大型语言模型 (LLM) 的法律分析能力对于加强法律服务和人工智能治理至关重要.
- 税法为LLM推理和数学技能的自动验证和测试提供了一个结构化的领域.
研究的目的:
- 探索和评估LLMs在应用税法方面的能力.
- 评估法律背景和提示策略对法律推理中的LLM绩效的影响.
主要方法:
- 使用连续的OpenAI模型发布进行了实验,以跟踪性能改进.
- 该研究涉及检索和利用相关的法律权威,为LLMs提供背景.
- 通过使用问题答案对的Few-shot提示技术来提高模型的性能.
主要成果:
- 法律法学士证明了新兴的法律理解,性能随着新的OpenAI模型的增加而增加.
- 提供了额外的法律背景,并采用了少数拍摄的提示,大大提高了LLM的准确性,特别是对于GPT-4.
- 目前在税法领域的LLM成绩是准确的,但尚未与专家税务律师的成绩相匹配.
结论:
- 法律法学显示出法律分析的巨大潜力,特别是在税法等结构化领域.
- 在LLMs的进步,加上有效的提示和上下文信息,可以导致法律任务的高准确性.
- 未来LLM的自主法律推理能力可能会对法律职业和AI治理框架产生深远的影响.
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