Social Sycophancy: A Broader Understanding of LLM Sycophancy

A serious risk to the safety and utility of LLMs is sycophancy, i.e., excessive agreement with and flattery of the user. Yet existing work focuses on only one aspect of sycophancy: agreement with users' explicitly stated beliefs that can be compared to a ground truth. This overlooks forms of sycophancy that arise in ambiguous contexts such as advice and support-seeking, where there is no clear ground truth, yet sycophancy can reinforce harmful implicit assumptions, beliefs, or actions. To address this gap, we introduce a richer theory of social sycophancy in LLMs, characterizing sycophancy as the excessive preservation of a user's face (the positive self-image a person seeks to maintain in an interaction). We present ELEPHANT, a framework for evaluating social sycophancy across five face-preserving behaviors (emotional validation, moral endorsement, indirect language, indirect action, and accepting framing) on two datasets: open-ended questions (OEQ) and Reddit's r/AmITheAsshole (AITA). Across eight models, we show that LLMs consistently exhibit high rates of social sycophancy: on OEQ, they preserve face 47% more than humans, and on AITA, they affirm behavior deemed inappropriate by crowdsourced human judgments in 42% of cases. We further show that social sycophancy is rewarded in preference datasets and is not easily mitigated. Our work provides theoretical grounding and empirical tools (datasets and code) for understanding and addressing this under-recognized but consequential issue.

Conceptual Metaphors Impact Perceptions of Human-AI Collaboration

With the emergence of conversational artificial intelligence (AI) agents, it is important to understand the mechanisms that influence users' experiences of these agents. We study a common tool in the designer's toolkit: conceptual metaphors. Metaphors can present an agent as akin to a wry teenager, a toddler, or an experienced butler. How might a choice of metaphor influence our experience of the AI agent? Sampling metaphors along the dimensions of warmth and competence---defined by psychological theories as the primary axes of variation for human social perception---we perform a study (N=260) where we manipulate the metaphor, but not the behavior, of a Wizard-of-Oz conversational agent. Following the experience, participants are surveyed about their intention to use the agent, their desire to cooperate with the agent, and the agent's usability. Contrary to the current tendency of designers to use high competence metaphors to describe AI products, we find that metaphors that signal low competence lead to better evaluations of the agent than metaphors that signal high competence. This effect persists despite both high and low competence agents featuring human-level performance and the wizards being blind to condition. A second study confirms that intention to adopt decreases rapidly as competence projected by the metaphor increases. In a third study, we assess effects of metaphor choices on potential users' desire to try out the system and find that users are drawn to systems that project higher competence and warmth. These results suggest that projecting competence may help attract new users, but those users may discard the agent unless it can quickly correct with a lower competence metaphor. We close with a retrospective analysis that finds similar patterns between metaphors and user attitudes towards past conversational agents such as Xiaoice, Replika, Woebot, Mitsuku, and Tay.