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.

通过对大规模跨文化研究的启发，我们对大型语言模型进行了道德困境的决策制定，发现其中在不同语言环境下，模型与人类偏好吻合程度存在差异，并对模型的道德选择进行了解释和发展水平的评估。

多语言语言模型的哲学思考

Multilingual Trolley Problems for Language Models

Perception of offensiveness is inherently subjective, shaped by the lived
experiences and socio-cultural values of the perceivers. Recent years have seen
substantial efforts to build AI-based tools that can detect offensive language
at scale, as a means to moderate social media platforms, and to ensure safety
of conversational AI technologies such as ChatGPT and Bard. However, existing
approaches treat this task as a technical endeavor, built on top of data
annotated for offensiveness by a global crowd workforce without any attention
to the crowd workers' provenance or the values their perceptions reflect. We
argue that cultural and psychological factors play a vital role in the
cognitive processing of offensiveness, which is critical to consider in this
context. We re-frame the task of determining offensiveness as essentially a
matter of moral judgment -- deciding the boundaries of ethically wrong vs.
right language within an implied set of socio-cultural norms. Through a
large-scale cross-cultural study based on 4309 participants from 21 countries
across 8 cultural regions, we demonstrate substantial cross-cultural
differences in perceptions of offensiveness. More importantly, we find that
individual moral values play a crucial role in shaping these variations: moral
concerns about Care and Purity are significant mediating factors driving
cross-cultural differences. These insights are of crucial importance as we
build AI models for the pluralistic world, where the values they espouse should
aim to respect and account for moral values in diverse geo-cultural contexts.

经过大规模的跨文化研究，我们发现不同文化背景下对冒犯性的感知存在显著差异，而个体的道德价值观在塑造这些差异方面起到了关键作用。这些认识对于我们构建适用于多元文化背景的 AI 模型至关重要，以确保它们尊重和考虑各种地理文化背景中的道德价值观。