大型语言模型在法律文本的零射击语义注释中的不合理有效性
Jaromir Savelka1, Kevin D Ashley2
1School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, United States.
Frontiers in artificial intelligence
|December 4, 2023
概括
像GPT-4这样的较新的大型语言模型 (LLM) 在法律文本语义注释任务中表现得更好. 具有成本效益的GPT-3.5轮机与旧型号相匹配,为法律专业人士提供实用见解.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 法律信息学 法律信息学
背景情况:
- 大型语言模型 (LLM) 在文件起草和总结等法律应用中显示出潜力.
- 最近的进展需要重新评估LLM在法律文本语义注释方面的表现.
- 生成型人工智能系统正在成熟,需要对其能力进行更新分析.
研究的目的:
- 分析GPT-4和GPT-3.5-turbo在法律文本上的语义注释性能.
- 为了在零射击学习环境中比较较新的LLM与上一代的LLM.
- 评估不同LLM模型的法律应用的性能-成本权衡.
主要方法:
- 检查了GPT-4和GPT-3.5-turbo (((-16k) 在三个不同的法律文本注释任务上的性能.
- 将较新的模型与较旧的GPT代相比较 (例如,text-davinci-003).
- 在提示符中根据批量大小评估性能变化.
主要成果:
- 在三个注释任务中,GPT-4在两个任务中显著超过了GPT-3.5模型.
- GPT-3.5-turbo表现出与更昂贵的text-davinci-003.3相比的性能.
- 在单个提示中,LLM性能随着更大的批量大小而下降.
结论:
- 对于特定的法律文本注释任务,GPT-4提供了卓越的性能.
- GPT-3.5-turbo提供了具有竞争力的性能,具有成本效益的替代方案.
- 调查结果指导了LLMs在法律工作流程中的整合,包括合同审查和实证法律研究.
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