释放快速工程的大型语言模型的潜力
Banghao Chen1,2,3, Zhaofeng Zhang1,4,5, Nicolas Langrené1,4
1Guangdong Provincial/Zhuhai Key Laboratory of Interdisciplinary Research and Application for Data Science, Beijing Normal-Hong Kong Baptist University, Zhuhai 519087, Guangdong, China.
Patterns (New York, N.Y.)
|June 27, 2025
概括
提示工程通过结构化输入优化大语言模型 (LLM) 和视觉语言模型 (VLM). 本综述涵盖了先进的技术和人工智能安全应用,以提高模型性能和稳定性.
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.
- 计算机视觉 计算机视觉
背景情况:
- 大型语言模型 (LLM) 和视觉语言模型 (VLM) 显示出显著的潜力.
- 为了最大限度地提高LLM/VLM的实用性和准确性,需要有效的输入结构.
- 快速工程已经成为提高模型性能的关键技术.
研究的目的:
- 探索快速工程在最大限度地提高LLM和VLM能力中的作用.
- 审查基础和先进的快速工程方法.
- 通过使用可靠的指标,检查VLM的快速方法及其有效性.
主要方法:
- 审查基础和先进的提示工程技术 (例如,自我一致性,思维链,生成知识).
- 具体针对视觉语言模型 (VLMs) 的提示方法的详细检查.
- 评估即时方法,使用主观和客观指标进行可靠的分析.
主要成果:
- 先进的提示工程技术显著提高了LLM和VLM的性能.
- 对于VLMs的快速方法是多样化的,需要具体的评估.
- 快速工程在防范敌对攻击的AI安全方面发挥着至关重要的作用.
结论:
- 快速工程对于释放LLM和VLM的全部潜力至关重要.
- 有效的及时设计和评估对于模型的准确性和安全性至关重要.
- 未来的研究应该专注于先进的提示策略和AI安全增强.
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