评估人工智能驱动的问答系统:确定需要专家评级的简单方法
Dorian Zwanzig1, Luca Kreibich1, Uta Binder1
1HTW Berlin, Faculty 4 (Computing, Communication and Business).
Studies in health technology and informatics
|October 3, 2025
概括
非专业人士和人工智能有时可以取代人工智能问答系统的专家评级者. 本研究引入了一种评估非专家评价者协议的方法,发现它们可以在某些场景中与专家可靠性相匹配.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 自然语言处理自然语言处理.
背景情况:
- 评估人工智能驱动的问答 (Q&A) 系统通常依赖于专家评级.
- 专家评估的成本和可扩展性可能是人工智能开发的瓶.
- 探索像非专业人士或人工智能这样的替代评级者对于有效的系统评估至关重要.
研究的目的:
- 引入一种简单的方法来评估非专业人士或人工智能的充分性,以取代人工智能问答系统的专家评级.
- 建立专家协议的基准,并将其与非专家评级人员的表现进行比较.
- 为评估评级者可靠性提供透明和结构化的方法.
主要方法:
- 利用加权的科恩卡帕来量化评级者之间的可靠性.
- 建立了一个专家协议基准以进行比较.
- 采用了互评级可靠性矩阵来可视化和分析结果.
主要成果:
- 研究结果表明,在特定情况下,普通人和人工智能可以达到与人类专家相美或超过的协议水平.
- 非专家评级人员的有效性尤其显著,当避免风险是考虑因素时.
- 拟议的方法在评估评级者充分性的过程中展示了透明度和结构性.
结论:
- 开发的方法提供了一种可行的和可适应的方法,用于评估非专家评级人员在AI问答系统评估中的适用性.
- 非专业人士和人工智能评估者显示出增强或取代专家评估的潜力,特别是在风险敏感的应用中.
- 该方法的灵活性允许在各种AI评估环境和评级标准中进行应用.
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