Large Language Models (LLMs), such as GPT-3, have demonstrated remarkable natural language processing and generation capabilities and have been applied to a variety tasks, such as source code generation. This paper explores the potential of integrating LLMs in the hazard analysis for safety-critical systems, a process which we refer to as co-hazard analysis (CoHA). In CoHA, a human analyst interacts with an LLM via a context-aware chat session and uses the responses to support elicitation of possible hazard causes. In this experiment, we explore CoHA with three increasingly complex versions of a simple system, using Open AI's ChatGPT service. The quality of ChatGPT's responses were systematically assessed to determine the feasibility of CoHA given the current state of LLM technology. The results suggest that LLMs may be useful for supporting human analysts performing hazard analysis.

翻译：大型语言模型（LLMs），例如GPT-3，已展现出卓越的自然语言处理与生成能力，并被应用于源代码生成等多种任务。本文探索了将LLMs整合到安全关键系统的危害分析中的潜力，我们将该过程称为协同危害分析（CoHA）。在CoHA中，人类分析员通过上下文感知的聊天会话与LLM交互，并利用其响应来支持可能的危害原因引发。在本实验中，我们使用OpenAI的ChatGPT服务，针对一个简单系统的三个逐步复杂版本进行了CoHA探索。我们系统评估了ChatGPT响应的质量，以判定在当前LLM技术状态下CoHA的可行性。结果表明，LLMs可能有助于支持人类分析员执行危害分析。