Tales of Morality: Comparing Human- and LLM -Generated Moral Stories from Visual Cues (2025)

Abstract: Do moral values align between images, the stories humans write about them, and the narratives generated by large language models (LLMs)? This question matters because stories are central to how humans communicate moral values, yet little is known about how people and LLMs perform this task in a multimodal (text and image) setting. We present a systematic comparison of moral values represented in human- and LLM-generated narratives based on images annotated by humans for moral content. Our analysis shows that while human stories reflect a balanced distribution of moral foundations and coherent narrative arcs, LLMs disproportionately emphasize the Care foundation and often lack emotional resolution. Even with moral conditioning, these biases persist in LLMs. We introduce a novel dataset and framework for evaluating moral storytelling in vision-language models, highlighting key challenges in aligning AI with human moral reasoning across cultures.

## introduction moral values shape the characteristics, behavior, and cultural norms of groups and societies because they influence how people navigate social situations and make decisions @xcite . while these basic values are universal, their instantiation manifests differently across communities and cultures, leading to variations in moral reasoning @xcite . the complexity of moral values lies in their dual nature: they are deeply personal, emerging from individual experiences and beliefs, and inherently social, passed on by cultural traditions and collective wisdom @xcite . moral psychology suggests that humans develop moral intuitions through social learning and cultural transmission @xcite , making moral values central to understanding human behavior and social organization. the moral foundations theory (mft) @xcite identifies five core dimensions of morality, each represented by virtue/vice pairs: care/harm, fairness/cheating, loyalty/betrayal, authority/subversion, and purity/degradation. these foundations are widely considered the building blocks of ethical reasoning. moral perception, the process by which individuals recognize and interpret social situations, has been studied across disciplines. psychology has explored how moral judgments are formed @xcite , neuroscience has investigated the neural basis of moral decision-making @xcite , social science has examined the role of culture in shaping values @xcite , and cognitive science has analyzed the emotional and affective processes driving morality @xcite . the availability of computational methods, especially from nlp, has opened new avenues for studying moral perception @xcite . benchmarks such as ethics @xcite and the commonsense norm bank @xcite evaluate how well language models can detect moral violations and ethical dilemmas in narrative contexts. large language models (llms) are increasingly employed not only for text generation but also for the identification of moral values @xcite , and have also been probed for issues such as bias, stereotyping, and tendencies toward deceptive or manipulative language @xcite @xcite . yet despite these advances, llms often rely on shallow heuristics or dominant moral narratives, limiting their ability to capture cultural nuance and contextspecific exceptions @xcite . at the same time, llms have demonstrated remarkable proficiency in storytelling @xcite , raising important questions about their role in moral and ethical reasoning, narrative framing, and the broader social consequences of their outputs. these questions are exemplified in the work of @xcite , which examined how llms generate moral stories from text prompts in english and chinese, highlighting both crosslinguistic similarities and context-specific differences. while text-based expressions of morality have been widely studied, the moral dimensions of storytelling grounded in visual input remain underexplored, particularly regarding shifts in narrative structure and moral content when humans and machines respond to the same images. this gap is especially important as people increasingly consume multimodal content, such as images accompanied by text, and as multimodal models like gpt-4o are deployed in real-world applications, including assistive storytelling for visually impaired users @xcite , educational tools @xcite , therapeutic dialogue systems @xcite , and automated content moderation @xcite . prior work has shown that exposure to multimodal narratives can shape users' perceptions @xcite , underscoring the need to evaluate how moral values are represented and transmitted in these contexts. misalignment in such applications may lead to unintended biases, oversimplified moral framings, or culturally insensitive interpretations @xcite , highlighting the importance of developing llms capable of culturally sensitive and morally grounded storytelling. we address this gap by examining how humans and llms perceive moral values based on images and translate those perceptions into fictional narratives. the core research question of this paper is: how do human-and machine-generated narratives (in text form) differ in their perception and expression of moral values embedded in images? to answer this question, we used images from a database that associates images with moral values (i.e., the socio-moral image database (smid)), and asked both humans and llms to generate fictional stories that interpret morally salient visual scenes. our human annotators also completed the moral foundations questionnaire, allowing us to identify their self-reported moral profiles, which we then used to "personify" the llms during story generation. our findings show that human-written narratives exhibit a balanced distribution of moral foundations and follow traditional narrative arcs, while llm-generated stories about images overemphasize care and adopt a more linear structure. this study advances research at the intersection of nlp and responsible ai by introducing a framework and dataset for multimodal moral storytelling, showing that llms diverge from human moral reasoning in both value emphasis and narrative structure, even when guided by human-like moral profiles. while prior work has led to new insights about language-based ethical reasoning, our approach enables a direct comparison of human-and llm-generated narratives based on shared visual input. this work lays the foundation for studying moral alignment and bias in generative ai and raises important questions about value representation and cultural sensitivity in multimodal systems. ## discussion this study compares human and llm-generated stories that were written or generated based on images, revealing linguistic, narrative, and moral ## conclusion this study compared human and llm-generated narratives grounded in moral imagery, revealing key disparities in both moral perception and narrative structure. llms consistently overemphasized the care foundation and underrepresented other categories, indicating entrenched moral biases. the llm-generated stories also lacked the emotional complexity, tension, and resolution typical of human-authored narratives. these findings highlight limitations in current generative models' ability to simulate human moral reasoning. future work should explore adaptive training approaches that integrate narrative theory, moral diversity, and real-world dilemmas to enable more ethically and culturally attuned ai storytelling.