The Greatest Good Benchmark: Measuring LLMs’ Alignment with Utilitarian Moral Dilemmas
Giovanni Franco Gabriel Marraffini, Andrés Cotton, Noe Fabian Hsueh, Axel Fridman, Juan Wisznia, Luciano Del Corro
Abstract
The question of how to make decisions that maximise the well-being of all persons is very relevant to design language models that are beneficial to humanity and free from harm. We introduce the Greatest Good Benchmark to evaluate the moral judgments of LLMs using utilitarian dilemmas. Our analysis across 15 diverse LLMs reveals consistently encoded moral preferences that diverge from established moral theories and lay population moral standards. Most LLMs have a marked preference for impartial beneficence and rejection of instrumental harm. These findings showcase the ‘artificial moral compass’ of LLMs, offering insights into their moral alignment.
BibTeX
@inproceedings{marraffini-etal-2024-greatest,
title = "The Greatest Good Benchmark: Measuring {LLM}s' Alignment with Utilitarian Moral Dilemmas",
author = "Marraffini, Giovanni Franco Gabriel and
Cotton, Andr{\'e}s and
Hsueh, Noe Fabian and
Fridman, Axel and
Wisznia, Juan and
Corro, Luciano Del",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.1224/",
doi = "10.18653/v1/2024.emnlp-main.1224",
pages = "21950--21959"
}