解码多语言道德偏好：通过道德机器实验揭示大语言模型的偏见
Decoding Multilingual Moral Preferences: Unveiling LLM's Biases Through the Moral Machine Experiment
AI总结：
本研究基于道德机器实验，调查五个大语言模型在十种语言下的道德偏好，发现它们存在跨语言偏见且与人类价值观显著偏离，如Llama 3偏好拯救少数人。
摘要：
大语言模型（LLMs）越来越多地进入我们日常生活的各个领域。它们通过日常使用间接影响人们的决策或观点。因此，理解这些LLM如何以及做出何种道德判断至关重要。然而，道德并非普适的，而是依赖于文化背景。这就提出了一个问题：当以不同语言提示时，这些文化偏好是否也反映在LLM中，还是道德决策在不同语言间保持一致？迄今为止，大多数研究集中于调查LLM在英语中的固有价值观。虽然少数工作在多语言环境中对LLM的道德偏见进行了多语言分析，但这些分析并未超越原子动作。据我们所知，针对困境中道德偏见的多语言分析尚未开展。\n为解决这一问题，我们的论文基于道德机器实验（MME）来调查五个LLM（Falcon、Gemini、Llama、GPT和MPT）在多语言环境中的道德偏好，并将它们与来自不同文化的人类偏好进行比较。为实现这一目标，我们生成了6500个MME场景，并用十种语言提示模型应采取何种行动。我们的分析揭示，所有LLM都在一定程度上表现出不同的道德偏见，它们不仅与人类偏好不同，而且在模型自身内部跨语言也存在差异。此外，我们发现几乎所有模型，特别是Llama 3，都极大地偏离了人类价值观，例如，偏好拯救更少的人而非拯救更多的人。
英文摘要：
Large language models (LLMs) increasingly find their way into the most diverse areas of our everyday lives. They indirectly influence people's decisions or opinions through their daily use. Therefore, understanding how and which moral judgements these LLMs make is crucial. However, morality is not universal and depends on the cultural background. This raises the question of whether these cultural preferences are also reflected in LLMs when prompted in different languages or whether moral decision-making is consistent across different languages. So far, most research has focused on investigating the inherent values of LLMs in English. While a few works conduct multilingual analyses of moral bias in LLMs in a multilingual setting, these analyses do not go beyond atomic actions. To the best of our knowledge, a multilingual analysis of moral bias in dilemmas has not yet been conducted. To address this, our paper builds on the moral machine experiment (MME) to investigate the moral preferences of five LLMs, Falcon, Gemini, Llama, GPT, and MPT, in a multilingual setting and compares them with the preferences collected from humans belonging to different cultures. To accomplish this, we generate 6500 scenarios of the MME and prompt the models in ten languages on which action to take. Our analysis reveals that all LLMs inhibit different moral biases to some degree and that they not only differ from the human preferences but also across multiple languages within the models themselves. Moreover, we find that almost all models, particularly Llama 3, divert greatly from human values and, for instance, prefer saving fewer people over saving more.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
