在LLMs时代的作者归因:问题,方法和挑战
Baixiang Huang1, Canyu Chen1, Kai Shu2
1Illinois Institute of Technology, Chicago, IL, USA.
概括
作者归属方法受到大型语言模型 (LLM) 的挑战. 本综述对归因问题进行了分类,并讨论了LLM生成文本的概括性和可解释性.
科学领域:
- 数字法医学数字法医学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 准确的作者归属对于数字内容的完整性,法医调查以及打击错误信息和抄袭至关重要.
- 大型语言模型 (LLM) 的兴起使传统的作者归因变得复杂,模糊了人类和机器生成文本之间的界限.
- 在数字时代,建立作者信誉和问责制是必不可少的.
研究的目的:
- 在大型语言模型 (LLM) 的背景下,提供关于作者归因的综合文献综述.
- 系统地探索和分类当前的作者归因的研究挑战.
- 在这个快速发展的领域中确定未来的研究方向和开放问题.
主要方法:
- 对作者归因的最新研究进行系统的文献综述.
- 将归因问题的分类划分为四个关键领域:人写的文本归因,LLM生成的文本检测,LLM生成的文本归因和人-LLM共同撰写的文本归因.
- 讨论与归因方法的概括和可解释性相关的挑战.
主要成果:
- 该研究将作者归因归类为四种不同的问题类型.
- 突出了跨领域概括和归因模型可解释性的关键挑战.
- 评估现有方法和基准以确定局限性.
结论:
- 文献综述是作者归因研究人员和从业人员的路线图.
- 需要进一步的研究来解决强大的归因的概括和可解释性挑战.
- 该研究确定了在法学士时代的关键开放问题和未来研究方向.
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