大型语言模型 (LLM) 和错误信息的制度化
Maryanne Garry1, Way Ming Chan1, Jeffrey Foster2
1Psychology, The University of Waikato, Hamilton, New Zealand.
Trends in cognitive sciences
|October 11, 2024
概括
大型语言模型 (LLM) 传播了令人信服的错误信息,可能会侵蚀我们区分真相和虚假的能力. 这可能会破坏现实监测,影响决策和社会信任.
科学领域:
- 心理学科学 心理学科学
- 人工智能伦理学 人工智能伦理学
背景情况:
- 像ChatGPT这样的大型语言模型 (LLM) 产生了大量的在线信息,将真相与虚假混合在一起.
- 根据心理学科学的信息,LLM产生的内容具有说服力的性质,可以导致用户接受错误信息作为事实.
研究的目的:
- 检查LLM产生的错误信息对用户现实监控能力的潜在影响.
- 探索用户生成的错误信息被新兴的LLM所采用的反循环,加剧了这个问题.
主要方法:
- 对LLM内容生成技术的分析.
- 对影响信念形成的心理原则的审查.
- 通过LLM网络传播错误信息的概念建模.
主要成果:
- 法律法规可以大规模传播令人信服的错误信息.
- 存在一个反循环,错误信息可以被LLMs放大.
- 现实监控的侵蚀可能会发生,导致依赖虚假信息.
结论:
- 由LLM产生的错误信息的扩散对个人和社会功能构成重大威胁.
- 失去现实监测可以导致错误的决策,对机构的信任下降,以及社会分裂.
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