  Assurance cases (ACs) are structured arguments that support the verification
of the correct implementation of systems' non-functional requirements, such as
safety and security, thereby preventing system failures which could lead to
catastrophic outcomes, including loss of lives. ACs facilitate the
certification of systems in accordance with industrial standards, for example,
DO-178C and ISO 26262. Identifying defeaters arguments that refute these ACs is
essential for improving the robustness and confidence in ACs. To automate this
task, we introduce a novel method that leverages the capabilities of GPT-4
Turbo, an advanced Large Language Model (LLM) developed by OpenAI, to identify
defeaters within ACs formalized using the Eliminative Argumentation (EA)
notation. Our initial evaluation gauges the model's proficiency in
understanding and generating arguments within this framework. The findings
indicate that GPT-4 Turbo excels in EA notation and is capable of generating
various types of defeaters.
