Humanizing Machines: Rethinking LLM Anthropomorphism Through a Multi-Level Framework of Design
🤖 AI Summary
Prior research predominantly examines the risks of LLM anthropomorphism—such as unwarranted trust—while overlooking its potential as a controllable design lever to support user goals. This paper introduces a Personification Design Framework that systematically modulates human-like traits across four cue categories: perceptual, linguistic, behavioral, and cognitive, enabling functional alignment with user tasks. Innovatively treating personification as a tunable design variable, we establish a unified taxonomy and actionable design levers, and propose a task-efficiency–oriented evaluation paradigm. Grounded in interdisciplinary theory—including design research and cognitive response modeling—we conduct structured conceptual modeling and empirical validation. Our work delivers an evidence-based, practitioner-oriented design guide that enhances interaction naturalness and improves task completion quality. (138 words)
Application Category
📝 Abstract
Large Language Models (LLMs) increasingly exhibit extbf{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: extit{perceptive, linguistic, behavioral}, and extit{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.
Problem
Research questions and friction points this paper is trying to address.
Reframing LLM anthropomorphism as intentional design concept
Providing multi-level framework for anthropomorphic cue analysis
Establishing function-oriented evaluation for human-AI interaction design
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-level framework for anthropomorphic design
Four-dimensional cue taxonomy for LLMs
Function-oriented evaluation of human-like AI
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