大型语言模型在产品风险评估方面有多好?
Zachary A Collier1, Richard J Gruss1, Alan S Abrahams2
1Department of Management, Radford University, Radford, Virginia, USA.
概括
像ChatGPT一样,生成人工智能 (AI) 可以帮助产品安全专业人员进行头脑风暴,思考潜在的故障模式和降低风险. 然而,人工智能产生的内容需要专家审查,因为潜在的错误和产品风险评估的通用指导.
科学领域:
- 产品安全工程 产品安全工程
- 人工智能在风险管理中的应用
背景情况:
- 产品安全专业人员负责评估产品使用和滥用对消费者的风险.
- 生成型人工智能 (AI),特别是大型语言模型 (LLM),为增强产品风险评估过程提供了潜在的工具.
研究的目的:
- 调查LLM,如ChatGPT在各种产品风险评估任务中的实用性.
- 评估LLM在识别故障模式,进行故障模式和影响分析 (FMEA) 和建议降低风险方面的表现.
主要方法:
- 开发了六种消费者产品的提示,包括故障模式识别,FMEA表的创建和风险减轻策略.
- 输入提示进入ChatGPT和其他LLM,记录生成的输出.
- 对产品安全专业人员进行调查,以评估人工智能产生的输出质量和实用性.
主要成果:
- 在不同的思维任务中,LLM表现出强项,例如进行头脑风暴,思考潜在的故障模式和降低风险.
- 在人工智能产生的结果中发现了错误和不一致,指导通常被认为是通用的或缺乏专家深度.
- 在测试的不同LLM中观察到类似的性能模式.
结论:
- 在产品风险评估中,LLM显示出潜在的辅助工具,特别是用于头脑风暴.
- 人类专业知识对于对人工智能产生的产品安全内容进行批判性审查和验证至关重要.
- 人工智能的作用可能会演变为支持专家,将他们的重点转向更高级别的分析和验证.
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