Large language models are increasingly used to simulate social and political audiences, but it remains unclear whether persona-conditioned generations provide coherent reception patterns or align with real public opinion. We study audience reactions to morally salient political messages, using Trump’s posts as a relevant case in point, through two theory-grounded human value frameworks, Inglehart-Welzel cultural map and Moral Foundations Theory. Across issue-specific message bundles, we analyze stance together with agreement, legitimacy, offensiveness, and endorsement, asking whether synthetic personas produce systematic reception differences, which persona dimensions explain those differences, and whether aggregate synthetic distributions align with matched Pew public-opinion patterns. The paper examines three related dimensions of synthetic-audience evaluation: internal differentiation, explanatory structure, and external plausibility. It contributes a reproducible study design for political audience simulation and an empirical account of where synthetic moral audiences are informative, and where they should not be treated as calibrated population proxies.

Morality serves as the foundation of societal structure, guiding legal systems, shaping cultural values, and influencing individual self-perception. With the rise and pervasiveness of generative AI tools, and particularly Large Language Models (LLMs), concerns arise regarding how these tools capture and potentially alter moral dimensions through machine-generated text manipulation. Based on the Moral Foundation Theory, our work investigates this topic by analyzing the behavior of 12 LLMs among the most widely used Open and uncensored (i.e., ”abliterated”) models, and leveraging human-annotated datasets used in moral-related analysis. Results have shown varying levels of alteration of moral expressions depending on the type of text modification task and moral-related conditioning prompt.

Verbs form the backbone of language, providing the structure and meaning to sentences. Yet, their intricate semantic nuances pose a longstanding challenge. Understanding verb relations through the concept of lexical entailment is crucial for comprehending sentence meanings and grasping verb dynamics. This work investigates the capabilities of eight Large Language Models in recognizing lexical entailment relations among verbs through differently devised prompting strategies and zero-/few-shot settings over verb pairs from two lexical databases, namely WordNet and HyperLex. Our findings unveil that the models can tackle the lexical entailment recognition task with moderately good performance, although at varying degree of effectiveness and under different conditions. Also, utilizing few-shot prompting can enhance the models’ performance. However, perfectly solving the task arises as an unmet challenge for all examined LLMs, which raises an emergence for further research developments on this topic.