Humanizing Machines: Rethinking LLM Anthropomorphism Through a Multi-Level Framework of Design

TL;DR

This paper proposes a multi-level framework for intentionally designing anthropomorphic qualities in Large Language Models, emphasizing user-centered cues and function-oriented evaluation to improve human-AI interaction.

Contribution

It introduces a novel multi-level framework that conceptualizes LLM anthropomorphism as a design choice, with a taxonomy of cues and actionable guidelines for practitioners.

Findings

Categorizes anthropomorphic cues into perceptive, linguistic, behavioral, and cognitive dimensions.

Provides a unified taxonomy and actionable levers for designing anthropomorphic features.

Advocates for function-oriented evaluation of anthropomorphic design.

Abstract

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.