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To support positive, ethical human-robot interactions, robots need to be able to respond to unexpected situations in which societal norms are violated, including rejecting unethical commands. Implementing robust communication for robots is inherently difficult due to the variability of context in real-world settings and the risks of unintended influence during robots’ communication. HRI researchers have begun exploring the potential use of LLMs as a solution for language-based communication, which will require an in-depth understanding and evaluation of LLM applications in different contexts. In this work, we explore how an existing LLM responds to and reasons about a set of norm-violating requests in HRI contexts. We ask human participants to assess the performance of a hypothetical GPT-4-based robot on moral reasoning and explanatory language selection as it compares to human intuitions. Our findings suggest that while GPT-4 performs well at identifying norm violation requests and suggesting non-compliant responses, its flaws in not matching the linguistic preferences and context sensitivity of humans prevent it from being a comprehensive solution for moral communication between humans and robots. Based on our results, we provide a four-point recommendation for the community in incorporating LLMs into HRI systems.