ArticleRoyal Society open science2025
Robustness of large language models in moral judgements.
Article in Royal Society open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A validity-guided workflow for robust large language model research in psychology.Behavior research methods · 2026Article
- Algorithmic bias in clinical resource allocation by a large language model: a cross-sectional in-silico evaluation of 13,608 decisions.BMC medical ethics · 2026Article
- The AI interviewer: multi-faceted evaluation of adaptive questioning by large language models.Scientific reports · 2026Article
- Review
- Robustness of large language models in moral judgements.Royal Society open science · 2025Article
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2 authors.
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Abstract
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.
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