This paper investigates the challenges of affect control in large language models (LLMs), focusing on their ability to express appropriate emotional states during extended dialogues. We evaluated state-of-the-art open-weight LLMs to assess their affective expressive range in terms of arousal and valence. Our study employs a novel methodology combining LLM-based sentiment analysis with multiturn dialogue simulations between LLMs. We quantify the models' capacity to express a wide spectrum of emotions and how they fluctuate during interactions. Our findings reveal significant variations among LLMs in their ability to maintain consistent affect, with some models demonstrating more stable emotional trajectories than others. Furthermore, we identify key challenges in affect control, including difficulties in producing and maintaining extreme emotional states and limitations in adapting affect to changing conversational contexts. These findings have important implications for the development of more emotionally intelligent AI systems and highlight the need for improved affect modelling in LLMs.
LLMs can persuade only psychologically susceptible humans on societal issues, via trust in AI and emotional appeals, amid logical fallacies
organization= CogNosco Lab, Department of Psychology；Cognitive Science, University of Trento , city= Rovereto , country= Italy；organization= Institute of Information Science；Technologies ``Alessandro Faedo", National Research Council , country= Italy；organization= Thomas Lord Department of Computer Science, University of Southern California , city= Los Angeles , state= California , country= USA；organization= Department of Sociology；Social Research, University of Trento , city= Trento , country= Italy；organization= Centre for Behavioural；Implementation Sciences in Medicine (BISI), National University of Singapore , city= Singapore , country= Singapore
