Humanizing Machines: Rethinking LLM Anthropomorphism Through a Multi-Level Framework of Design
This work addresses the need for intentional anthropomorphic design in LLMs to enhance human-AI interactions, offering a structured approach for researchers and practitioners, though it is incremental in building on existing interdisciplinary insights.
The paper tackles the problem of limited design guidance for anthropomorphism in LLMs by proposing a multi-level framework that treats it as a design concept, categorizing cues into four dimensions to provide actionable levers for practitioners.
Large Language Models (LLMs) increasingly exhibit \textbf{anthropomorphism} characteristics -- human-like qualities portrayed across their outlook, language, behavior, and reasoning functions. Such characteristics enable more intuitive and engaging human-AI interactions. However, current research on anthropomorphism remains predominantly risk-focused, emphasizing over-trust and user deception while offering limited design guidance. We argue that anthropomorphism should instead be treated as a \emph{concept of design} that can be intentionally tuned to support user goals. Drawing from multiple disciplines, we propose that the anthropomorphism of an LLM-based artifact should reflect the interaction between artifact designers and interpreters. This interaction is facilitated by cues embedded in the artifact by the designers and the (cognitive) responses of the interpreters to the cues. Cues are categorized into four dimensions: \textit{perceptive, linguistic, behavioral}, and \textit{cognitive}. By analyzing the manifestation and effectiveness of each cue, we provide a unified taxonomy with actionable levers for practitioners. Consequently, we advocate for function-oriented evaluations of anthropomorphic design.