LLM在法律文书分析与生成中表现优异，但需优化提示工程以提升可靠性。
Aplicação de Large Language Models na Análise e Síntese de Documentos Jurídicos: Uma Revisão de Literatura
- 采用Few-shot、Zero-shot及思维链提示技术提升法律文本理解能力。
- GPT-4、BERT等模型在法律任务中效果显著，但存在幻觉和偏见问题。
- 适合法律AI研究者与司法科技从业者参考，关注提示工程改进。
大型语言模型（LLMs）在法律文书的分析与合成中日益广泛应用，实现了摘要生成、信息分类与检索等任务的自动化。本研究通过系统文献综述，探讨了提示工程在法律领域中的最新进展。结果显示，GPT-4、BERT、Llama 2和Legal-Pegasus等模型被广泛使用，Few-shot Learning、Zero-shot Learning及Chain-of-Thought prompting等技术有效提升了法律文本的解析能力。然而，模型偏见与生成幻觉仍制约其大规模应用。尽管LLMs在法律领域潜力巨大，仍需改进提示工程策略，以提高结果的准确性与可靠性。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have been increasingly used to optimize the analysis and synthesis of legal documents, enabling the automation of tasks such as summarization, classification, and retrieval of legal information. This study aims to conduct a systematic literature review to identify the state of the art in prompt engineering applied to LLMs in the legal context. The results indicate that models such as GPT-4, BERT, Llama 2, and Legal-Pegasus are widely employed in the legal field, and techniques such as Few-shot Learning, Zero-shot Learning, and Chain-of-Thought prompting have proven effective in improving the interpretation of legal texts. However, challenges such as biases in models and hallucinations still hinder their large-scale implementation. It is concluded that, despite the great potential of LLMs for the legal field, there is a need to improve prompt engineering strategies to ensure greater accuracy and reliability in the generated results.
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