Language Model Alignment in Multilingual Trolley Problems
On the map: LLM alignment / RLHF / preference optimisation → Human values and AI feedback alignment
Abstract (arXiv)
We evaluate the moral alignment of LLMs with human preferences in multilingual trolley problems. Building on the Moral Machine experiment, which captures over 40 million human judgments across 200+ countries, we develop a cross-lingual corpus of moral dilemma vignettes in over 100 languages called MultiTP. This dataset enables the assessment of LLMs' decision-making processes in diverse linguistic contexts. Our analysis explores the alignment of 19 different LLMs with human judgments, capturing preferences across six moral dimensions: species, gender, fitness, status, age, and the number of lives involved. By correlating these preferences with the demographic distribution of language speakers and examining the consistency of LLM responses to various prompt paraphrasings, our findings provide insights into cross-lingual and ethical biases of LLMs and their intersection. We discover significant variance in alignment across languages, challenging the assumption of uniform moral reasoning in AI systems and highlighting the importance of incorporating diverse perspectives in AI ethics. The results underscore the need for further research on the integration of multilingual dimensions in responsible AI research to ensure fair and equitable AI interactions worldwide. Our code and data are at https://github.com/causalNLP/moralmachine
Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 56 of its 85 references that are in the map. First posted on arXiv 2 Jul 2024.
Builds on
56 of 85 references.
Closest ancestors · 5
Same direction, newest first
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- 2022Constitutional AI: Harmlessness from AI Feedback15 Dec
- 2020Aligning AI With Shared Human Values5 Aug
Other references · 51
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50 most cited of 51 All 51 (JSON)
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Citing papers per year: 2025: 20; 2026: 26.
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- 2025Reward Model Perspectives: Whose Opinions Do Reward Models Reward?7 Oct 2025
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12 most cited of 28 All 28 (JSON)
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- 2026The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment6 Jul 2026
- 2026Can LLMs Imagine Moral Alternatives Beyond Binary Dilemmas?30 Jun 2026
- 2026Every Act Has Its Price: Compressed Moral Composition in Frontier LLMs29 May 2026
- 2026The Counterexample Game: Iterated Conceptual Analysis and Repair in Language Models5 May 2026
- 2026Moral Sensitivity in LLMs: A Tiered Evaluation of Contextual Bias via Behavioral Profiling and Mechanistic Interpretability4 May 2026
- 2026Machine Behavior in Relational Moral Dilemmas: Moral Rightness, Predicted Human Behavior, and Model Decisions23 Apr 2026
- 2026CulturALL: Benchmarking Multilingual and Multicultural Competence of LLMs on Grounded Tasks21 Apr 2026
- 2026Evaluating Cooperation in LLM Social Groups through Elected Leadership13 Apr 2026
- 2026GAIN: A Benchmark for Goal-Aligned Decision-Making of Large Language Models under Imperfect Norms19 Mar 2026
- 2026Language Shapes Mental Health Evaluations in Large Language Models6 Mar 2026
12 newest of 28 All 28 (JSON)
Similar, not cited
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