Safety Analysis in the Era of Large Language Models: A Case Study of STPA using ChatGPT

Large Language Models (LLMs) [27], including Generative Pre-trained Transformer (GPT) [6] and Bidirectional Encoder Representations from Transformers (BERT) [13], have achieved state-of-theart performance on a wide range of Natural Language Processing (NLP) tasks. LLMs are gaining popularity and receiving increasing attention for their significant applications in knowledge reasoning [12, 52, 57]. ChatGPT is one of the LLMs applications, and probably the application, in the limelight. ChatGPT was used for collating literature and writing professional papers in fields like law [9], and medical education [30, 16]. OpenAI announced GPT-4 in March 2023 that can pass some of the bar exams to AP Biology [39]. These successful stories demonstrate that people have already gained experience in using LLMs, for their performance in handling complex content due to their massive training datasets and model capacity to process and learn from data, enabling their potential for complex tasks that require domain expert knowledge [38]. Given this, as researchers in the field of safety-critical systems, we pose a question: Can safety analysis make use of LLMs?