Robustness of large language models in moral judgements

With the advent of large language models (LLMs), there has been a growing interest in analysing the preferences encoded in LLMs in the context of morality. Recent work has tested LLMs on various moral judgement tasks and drawn conclusions regarding the alignment between LLMs and humans. The present contribution critically assesses the validity of the method and results employed in previous work for eliciting moral judgements from LLMs. We find that previous results are confounded by biases in the presentation of the options in moral judgement tasks and that LLM responses are highly sensitive to prompt formulation variants as simple as changing 'Case 1' and 'Case 2' to '(A)' and '(B)'. Our results hence indicate that previous conclusions on moral judgements of LLMs cannot be upheld. We make recommendations for more sound methodological setups for future studies.