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 lowlevel 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 LLMgenerated 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. 1

Abstract One of the major outstanding questions in computational semantics is how humans integrate the meaning of individual words into a sentence in a way that enables understanding of complex and novel combinations of words, a phenomenon known as compositionality. Many approaches to modeling the process of compositionality can be classified as either “vector-based” models, in which the meaning of a sentence is represented as a vector of numbers, or “syntax-based” models, in which the meaning of a sentence is represented as a structured tree of labeled components. A major barrier in assessing and comparing these contrasting approaches is the lack of large, relevant datasets for model comparison. This article aims to address this gap by introducing a new dataset, STS3k, which consists of 2,800 pairs of sentences rated for semantic similarity by human participants. The sentence pairs have been selected to systematically vary different combinations of words, providing a rigorous test and enabling a clearer picture of the comparative strengths and weaknesses of vector-based and syntax-based methods. Our results show that when tested on the new STS3k dataset, state-of-the-art transformers poorly capture the pattern of human semantic similarity judgments, while even simple methods for combining syntax- and vector-based components into a novel hybrid model yield substantial improvements. We further show that this improvement is due to the ability of the hybrid model to replicate human sensitivity to specific changes in sentence structure. Our findings provide evidence for the value of integrating multiple methods to better reflect the way in which humans mentally represent compositional meaning.

To represent the meaning of a word, most models use external language resources, such as text corpora, to derive the distributional properties of word usage. In this study, we propose that internal language models, that are more closely aligned to the mental representations of words, can be used to derive new theoretical questions regarding the structure of the mental lexicon. A comparison with internal models also puts into perspective a number of assumptions underlying recently proposed distributional text-based models could provide important insights into cognitive science, including linguistics and artificial intelligence. We focus on word-embedding models which have been proposed to learn aspects of word meaning in a manner similar to humans and contrast them with internal language models derived from a new extensive data set of word associations. An evaluation using relatedness judgments shows that internal language models consistently outperform current state-of-the art text-based external language models. This suggests alternative approaches to represent word meaning using properties that aren't encoded in text.

Most distributional lexico-semantic models derive their representations based on external language resources such as text corpora. In this study, we propose that internal language models, that are more closely aligned to the mental representations of words could provide important insights into cognitive science, including linguistics. Doing so allows us to reflect upon theoretical questions regarding the structure of the mental lexicon, and also puts into perspective a number of assumptions underlying recently proposed distributional text-based models. In particular, we focus on word-embedding models which have been proposed to learn aspects of word meaning in a manner similar to humans. These are contrasted with internal language models derived from a new extensive data set of word associations. Using relatedness and similarity judgments we evaluate these models and find that the word-association-based internal language models consistently outperform current state-of-the art text-based external language models, often with a large margin. These results are not just a performance improvement; they also have implications for our understanding of how distributional knowledge is used by people.