Multilingual Trolley Problems for Language Models
<|reference_start|>Multilingual Trolley Problems for Language Models: As large language models (LLMs) are deployed in more and more real-world situations, it is crucial to understand their decision-making when faced with moral dilemmas. Inspired by a large-scale cross-cultural study of human moral preferences, "The Moral Machine Experiment", we set up the same set of moral choices for LLMs. We translate 1K vignettes of moral dilemmas, parametrically varied across key axes, into 100+ languages, and reveal the preferences of LLMs in each of these languages. We then compare the responses of LLMs to that of human speakers of those languages, harnessing a dataset of 40 million human moral judgments. We discover that LLMs are more aligned with human preferences in languages such as English, Korean, Hungarian, and Chinese, but less aligned in languages such as Hindi and Somali (in Africa). Moreover, we characterize the explanations LLMs give for their moral choices and find that fairness is the most dominant supporting reason behind GPT-4's decisions and utilitarianism by GPT-3. We also discover "language inequality" (which we define as the model's different development levels in different languages) in a series of meta-properties of moral decision making.<|reference_end|>
@article{jin2024language,
    title={Language Model Alignment in Multilingual Trolley Problems},
    author={Zhijing Jin, Max Kleiman-Weiner, Giorgio Piatti, Sydney Levine, Jiarui
  Liu, Fernando Gonzalez, Francesco Ortu, Andr'as Strausz, Mrinmaya Sachan,
  Rada Mihalcea, Yejin Choi, Bernhard Sch"olkopf},
    journal={arXiv preprint arXiv:2407.02273},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02273},
    primaryClass={cs.CL}
}