关于用大型语言模型解释建议:一篇回顾
1University of Gothenburg, Gothenburg, Sweden.
Frontiers in big data
|February 11, 2025
概括
大型语言模型 (LLM) 显示出可解释的推系统的前景. 然而,关于使用LLM用于推解释的研究仍处于早期阶段,目前只有很少的研究可用.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 信息检索 信息检索
背景情况:
- 像ChatGPT这样的大型语言模型 (LLM) 为增强推系统提供了新的途径.
- 可解释性对于用户的信任和建议的透明度至关重要.
研究的目的:
- 系统地审查使用LLM在推系统中生成解释的文献.
- 在这个新兴领域确定当前的方法,挑战和未来的研究方向.
主要方法:
- 在ACM计算机文学指南中进行了全面的文献搜索.
- 分析了2022年11月至2024年11月的出版物.
- 收录标准适用于232篇文章,产生了6篇相关研究.
主要成果:
- 在可解释的推系统中应用LLM是新生的,发表的研究有限.
- 审查的研究强调了LLMs在提高推解释的质量和透明度方面的潜力.
结论:
- 尽管研究处于早期阶段,但LLM对推系统中推进可解释AI具有重大潜力.
- 鼓励进一步的研究,以开发更加透明和以用户为中心的推解释解决方案.
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