As large language models (LLMs) become more deeply integrated into various sectors, understanding how they make moral judgments has become crucial, particularly in the realm of autonomous driving. This study utilized the Moral Machine framework to investigate the ethical decision-making tendencies of prominent LLMs, including GPT-3.5, GPT-4, PaLM 2, and Llama 2, comparing their responses to human preferences. While LLMs' and humans' preferences such as prioritizing humans over pets and favoring saving more lives are broadly aligned, PaLM 2 and Llama 2, especially, evidence distinct deviations. Additionally, despite the qualitative similarities between the LLM and human preferences, there are significant quantitative disparities, suggesting that LLMs might lean toward more uncompromising decisions, compared to the milder inclinations of humans. These insights elucidate the ethical frameworks of LLMs and their potential implications for autonomous driving.

翻译：随着大型语言模型（LLMs）在各领域的深度融合，理解其道德判断机制变得至关重要，尤其在自动驾驶领域。本研究借助道德机器框架，探究了GPT-3.5、GPT-4、PaLM 2和Llama 2等主流LLMs的伦理决策倾向，并将其与人类偏好进行对比。尽管LLMs与人类在优先保护人类而非宠物、倾向拯救更多生命等偏好上总体一致，但PaLM 2和Llama 2表现出显著偏差。此外，尽管LLM与人类偏好在定性上相似，但在定量上存在显著差异：相较于人类较为温和的倾向，LLMs可能更倾向于做出不妥协的决策。这些发现揭示了LLMs的伦理框架及其对自动驾驶的潜在影响。