2025/05/26 by Chaoyi Xiang, Chunhua Liu, Xiang, Chaoyi +5 · 4 citations
Computer Science · Neuroscience · Social Sciences · #Computation and Language (cs.CL) #Computational and Text Analysis Methods #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Psychology of Moral and Emotional Judgment
paper · pdf · doi:10.48550/arxiv.2505.19674
openalex publication_date 2025/05/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As the impact of large language models increases, understanding the moral values they reflect becomes ever more important. Assessing the nature of moral values as understood by these models via direct prompting is challenging due to potential leakage of human norms into model training data, and their sensitivity to prompt formulation. Instead, we propose to use word associations, which have been shown to reflect moral reasoning in humans, as low-level underlying representations to obtain a more robust picture of LLMs' moral reasoning. We study moral differences in associations from western English-speaking communities and LLMs trained predominantly on English data. First, we create a large dataset of LLM-generated word associations, resembling an existing data set of human word associations. Next, we propose a novel method to propagate moral values based on seed words derived from Moral Foundation Theory through the human and LLM-generated association graphs. Finally, we compare the resulting moral conceptualizations, highlighting detailed but systematic differences between moral values emerging from English speakers and LLM associations.