使用集合提示和上下文学习方法进行大规模的总结
Andrés Leiva-Araos1,2,3, Bady Gana4, Héctor Allende-Cid4,5,6
1Department of Computing, University of North Florida, Jacksonville, FL, 32224, USA.
Scientific reports
|March 26, 2025
概括
本研究使用大型语言模型 (LLM) 分析网络安全文献,揭示了信息保证 (IA) 几十年来在信息保证 (IA) 中的关键转变和新兴趋势. 一种集合方法改善了关键字定义和总结准确性.
科学领域:
- 信息保证信息的保证.
- 网络安全 网络安全
- 自然语言处理 (NLP) 是一种自然语言处理.
背景情况:
- 信息保证 (IA) 和网络安全领域由于技术进步和日益复杂的数字威胁而发生了重大变化.
- 对IA文献的全面分析对于了解其历史发展和未来发展轨迹至关重要.
研究的目的:
- 通过使用先进的NLP技术,对1967年至2024年的IA和网络安全文献进行全面分析.
- 随着时间的推移,在IA中识别焦点转变,关键突破和新兴领域.
- 评估大型语言模型 (LLM) 和集合方法在文献分析和总结中的有效性.
主要方法:
- 来自Scopus (1967-2024) 的超过62,000份文档库的分析.
- 使用BERTopic进行主题检测,并在年度和十年期间使用LLM进行自动总结.
- 应用有针对性的查询,文本数据分析和高级提示技术进行总结.
主要成果:
- 一组方法 (Ev2) 与传统的总结和基于密度的方法相比,表现优越.
- 在关键词定义任务中,改进幅度从16.7%到29.6%不等.
- 在总结生成的7个测试指标中,集体方法在5个方面表现优于集体方法,同时保持了文献统计的完整性.
结论:
- 这项研究揭示了信息保证在过去几十年中不断变化的重点.
- 确定了在IA和网络安全方面重要的关键突破和新兴领域.
- 在专业科学领域,LLM和合奏方法为大规模的文献分析提供了强大的计算工具.
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