Paula Akemi Aoyagui

Patrick Yung Kang Lee, Jessica Y. Bo, Zixin Zhao et al.

Individuals are turning to increasingly anthropomorphic, general-purpose chatbots for AI companionship, rather than roleplay-specific platforms. However, not much is known about how individuals perceive and conduct their relationships with general-purpose chatbots. We analyzed semi-structured interviews (n=13), survey responses (n=43), and community discussions on Reddit (41k+ posts and comments) to triangulate the internal dynamics, external influences, and steering strategies that shape AI companion relationships. We learned that individuals conceptualize their companions based on an interplay of their beliefs about the companion's own agency and the autonomy permitted by the platform, how they pursue interactions with the companion, and the perceived initiatives that the companion takes. In combination with the external factors that affect relationship dynamics, particularly model updates that can derail companion behaviour and stability, individuals make use of different types of steering strategies to preserve their relationship, for example, by setting behavioural instructions or porting to other AI platforms. We discuss implications for accountability and transparency in AI systems, where emotional connection competes with broader product objectives and safety constraints.

Aryan Shrivastava, Paula Akemi Aoyagui

Language models (LMs) are increasingly being integrated into a wide range of applications, yet the modern evaluation paradigm does not sufficiently reflect how they are actually being used. Current evaluations rely on benchmarks that often lack direct applicability to the real-world contexts in which LMs are being deployed. To address this gap, we propose Dimensional and Contextual Evaluation (DICE), an approach that evaluates LMs on granular, context-dependent dimensions. In this position paper, we begin by examining the insufficiency of existing LM benchmarks, highlighting their limited applicability to real-world use cases. Next, we propose a set of granular evaluation parameters that capture dimensions of LM behavior that are more meaningful to stakeholders across a variety of application domains. Specifically, we introduce the concept of context-agnostic parameters - such as robustness, coherence, and epistemic honesty - and context-specific parameters that must be tailored to the specific contextual constraints and demands of stakeholders choosing to deploy LMs into a particular setting. We then discuss potential approaches to operationalize this evaluation framework, finishing with the opportunities and challenges DICE presents to the LM evaluation landscape. Ultimately, this work serves as a practical and approachable starting point for context-specific and stakeholder-relevant evaluation of LMs.