Theory of Mind and Self-Presentation in Human-LLM Interactions

The use of large language models (LLMs), such as ChatGPT, for
social support and other activities is growing. LLM-based interactions require users to express themselves through text, a medium in which people’s distinct self-presentation styles (SPS) present themselves. While the divergence of people’s SPS is well-established, the effect of SPS on users’ LLM interactions has not been explored. In this position paper, we point to this gap by drawing on insights from prior work on people’s SPS online. Moreover, we discuss how Theory of Mind (ToM) can be used to increase our understanding of the possible effects of SPS on LLM output. Through this exploration, we shed light on how LLM responses are dependent on and sensitive to how people present themselves in their interactions with LLMs. We discuss the broader implications and suggest future research directions for HCI centred around people’s SPS in interacting with LLMs—providing concrete suggestions on how effects of SPS on LLM output can be empirically explored.