Ivan Hernandez ^* Amal Chekili

• Virginia Tech, Blacksburg, United States

The past year has seen the rise of many variants of large language model chatbots that all attempt to carry out verbal tasks requested by users. These chatbotsperform various collaborative tasks, such as brainstorming, question and answering, summarization, and holding other forms of conversations, embedding them within our daily society. As these AI assistants become increasingly integrated into societal structures, understanding people's perceptions towards them offers insights into how to better facilitate that integration, and how different our current understanding of human-human interactions parallels human-AI interactions. This project explores people's preferences towards responses generated by various chatbots. Leveraging a comprehensive dataset composed of thousands of pairwise comparisons of responses from 17 popular chatbots, the study applies multidimensional scaling (MDS) and property fitting (PROFIT) methodologies to uncover the dimensionality of why some models are similarly or dissimilarly preferred on average by people. In line with previous research on universal dimensions of social cognition, interactions with chatbots are predominantly perceived along two dimensions: warmth and competence. This research advances our understanding of the interface between technology and social psychology. As chatbots and AI become increasingly prevalent within societal interactions, we see that many of the same principles found in perceptions between humans can also apply to AI.