风险链评估 (CORE):在公共心理健康中,为更安全的大型语言模型提供一个框架
Lingyu Li1,2,3, Shuqi Kong1,2,3, Haiquan Zhao2
1Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Psychiatry and clinical neurosciences
|January 24, 2025
概括
大型语言模型 (LLM) 存在公共心理健康风险,从不公平到安全威胁. 一个新的框架,CORE (风险评估链),有助于通过技术和公共合作来评估和减轻这些挑战.
科学领域:
- 人工智能的人工智能
- 心理学 心理学 心理学
- 公共卫生 公共卫生
背景情况:
- 大型语言模型 (LLM) 展示了先进的自然语言能力,推动了广泛采用.
- 这种采用引发了重大的公共心理健康问题,包括不平等,耻辱,依赖,医疗风险和安全威胁.
- 了解人与LLM的互动对于解决这些新出现的问题至关重要.
研究的目的:
- 通过参与者网络框架探索人与LLM的互动.
- 识别和分类与公共心理健康的LLM采用相关的风险.
- 为风险评估和减轻提出一个结构化的框架.
主要方法:
- 使用演员-网络框架进行文献综述和理论分析.
- 开发一个风险分类系统 (通用,特定于环境,特定于用户,特定于用户和特定于环境).
- 引入了CORE (风险评估链) 框架.
主要成果:
- 确定了四类与LLM相关的公共心理健康风险,其复杂性越来越大.
- CORE框架提供了一种结构化的方法来评估和减轻这些风险.
- 负责任的LLM开发被视为技术和公共努力的连续.
结论:
- 心理健康从业者对于评估和调节LLM风险至关重要.
- 实践者与开发者之间的合作对于负责任的LLM部署至关重要.
- 需要进一步的实证研究和公共教育来理解和管理心理影响.
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