A Taxonomy of Linguistic Expressions That Contribute To Anthropomorphism of Language Technologies

2025-02-14

AI Summary

This paper addresses the pervasive anthropomorphization of language technologies—particularly large language models—by developing the first systematic, multi-level linguistic taxonomy that identifies and organizes textual patterns triggering human trait attribution. Integrating a literature review with authentic user interaction corpora, the study employs qualitative analysis, conceptual mapping, and iterative coding to establish core dimensions—including intentionality, affectivity, and sociability. The resulting framework provides the first formal classification of anthropomorphic expressions specifically tailored to language technologies, exposing the tension between anthropomorphic discourse and anthropocentrism while highlighting its latent dehumanizing risks. Validated through application in AI interface design optimization and technology ethics policy deliberations, the taxonomy demonstrates both theoretical rigor and practical scalability. (149 words)

Technology Category

• Natural Language Processing: (Large) Language Models
• Humans and AI: Learning Human Values and Preferences
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Recent attention to anthropomorphism -- the attribution of human-like qualities to non-human objects or entities -- of language technologies like LLMs has sparked renewed discussions about potential negative impacts of anthropomorphism. To productively discuss the impacts of this anthropomorphism and in what contexts it is appropriate, we need a shared vocabulary for the vast variety of ways that language can be anthropomorphic. In this work, we draw on existing literature and analyze empirical cases of user interactions with language technologies to develop a taxonomy of textual expressions that can contribute to anthropomorphism. We highlight challenges and tensions involved in understanding linguistic anthropomorphism, such as how all language is fundamentally human and how efforts to characterize and shift perceptions of humanness in machines can also dehumanize certain humans. We discuss ways that our taxonomy supports more precise and effective discussions of and decisions about anthropomorphism of language technologies.

Problem

• Develop taxonomy for anthropomorphic language expressions
• Analyze user interactions with language technologies
• Address challenges in linguistic anthropomorphism understanding

Innovation

• Develop taxonomy of anthropomorphic expressions
• Analyze user interactions with LLMs
• Address linguistic anthropomorphism challenges