Data and code on the Moral Machine experiment on large language models (LLMs)

As large language models (LLMs) have become more deeply integrated into various sectors, understanding how they make moral judgements has become crucial, particularly in the realm of autonomous driving. This study used 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, to compare their responses with human preferences. While LLMs' and humans' preferences such as prioritizing humans over pets and favouring 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 with the milder inclinations of humans. These insights elucidate the ethical frameworks of LLMs and their potential implications for autonomous driving.随着大语言模型（Large Language Model，LLM）与各行业的融合程度不断加深，探究其道德判断的运作机制已成为至关重要的议题，在自动驾驶领域尤为如此。本研究采用道德机器（Moral Machine）框架，针对GPT-3.5、GPT-4、PaLM 2及Llama 2等主流大语言模型的伦理决策倾向展开调研，并将其生成的响应与人类偏好进行对比分析。尽管大语言模型与人类的偏好大体一致，例如优先保护人类而非宠物、倾向于拯救更多生命，但PaLM 2与Llama 2尤其显现出显著的偏差。此外，尽管二者的偏好存在定性层面的相似性，但二者间存在显著的定量差异，这表明相较于人类更为温和的决策倾向，大语言模型可能更倾向于做出绝对化的决策。这些研究发现阐明了大语言模型的伦理框架及其对自动驾驶领域的潜在影响。

搜集汇总

背景与挑战

背景概述

该数据集提供了在大型语言模型上进行道德机器实验的数据和代码，旨在研究GPT-3.5、GPT-4、PaLM 2和Llama 2等模型在自动驾驶道德决策中的倾向。研究发现，LLMs与人类偏好总体一致，但某些模型存在显著偏差，且LLMs倾向于更绝对的决策。