mlsanityData to 7 Oct 2026

AnthroScore: A Computational Linguistic Measure of Anthropomorphism

arXiv 2402.02056 ↗First posted 3 Feb 2024cs.CL cs.AI cs.CYcited by 27

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Abstract (arXiv)

Anthropomorphism, or the attribution of human-like characteristics to non-human entities, has shaped conversations about the impacts and possibilities of technology. We present AnthroScore, an automatic metric of implicit anthropomorphism in language. We use a masked language model to quantify how non-human entities are implicitly framed as human by the surrounding context. We show that AnthroScore corresponds with human judgments of anthropomorphism and dimensions of anthropomorphism described in social science literature. Motivated by concerns of misleading anthropomorphism in computer science discourse, we use AnthroScore to analyze 15 years of research papers and downstream news articles. In research papers, we find that anthropomorphism has steadily increased over time, and that papers related to language models have the most anthropomorphism. Within ACL papers, temporal increases in anthropomorphism are correlated with key neural advancements. Building upon concerns of scientific misinformation in mass media, we identify higher levels of anthropomorphism in news headlines compared to the research papers they cite. Since AnthroScore is lexicon-free, it can be directly applied to a wide range of text sources.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 25 of its 130 references that are in the map. First posted on arXiv 3 Feb 2024.

Builds on

25 of 130 references.

References · 25

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13 more
  1. 2019Show Your Work: Improved Reporting of Experimental Resultscited by 164
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 11; 2026: 9.

27 papers.

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  1. 2024The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Modelscited by 136
  2. 2025ELEPHANT: Measuring and understanding social sycophancy in LLMscited by 115
  3. 2024Virtual Personas for Language Models via an Anthology of Backstoriescited by 49
  4. 2025Understanding Aha Moments: from External Observations to Internal Mechanismscited by 48
  5. 2025Multi-turn Evaluation of Anthropomorphic Behaviours in Large Language Modelscited by 25
  6. 2024Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliancecited by 15
  7. 2024Perceptions of Sentient AI and Other Digital Minds: Evidence from the AI, Morality, and Sentience (AIMS) Surveycited by 15
  8. 2025The AI Gap: How Socioeconomic Status Affects Language Technology Interactionscited by 15
  9. 2025Thinking beyond the anthropomorphic paradigm benefits LLM researchcited by 13
  10. 2025HumT DumT: Measuring and controlling human-like language in LLMscited by 11
  11. 2025Dehumanizing Machines: Mitigating Anthropomorphic Behaviors in Text Generation Systemscited by 11
  12. 2025From tools to thieves: Measuring and understanding public perceptions of AI through crowdsourced metaphorscited by 9
15 more
  1. 2025Do Psychometric Tests Work for Large Language Models? Evaluation of Tests on Sexism, Racism, and Moralitycited by 9
  2. 2024"I Am the One and Only, Your Cyber BFF": Understanding the Impact of GenAI Requires Understanding the Impact of Anthropomorphic AIcited by 7
  3. 2025Humanlike AI Design Increases Anthropomorphism but Yields Divergent Outcomes on Engagement and Trust Globallycited by 6
  4. 2026SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancycited by 6
  5. 2025Sycophantic AI Decreases Prosocial Intentions and Promotes Dependencecited by 5
  6. 2024How ChatGPT Changed the Media's Narratives on AI: A Semi-Automated Narrative Analysis Through Frame Semanticscited by 4
  7. 2026Identity, Cooperation and Framing Effects within Groups of Real and Simulated Humanscited by 4
  8. 2024From Pixels to Personas: Investigating and Modeling Self-Anthropomorphism in Human-Robot Dialoguescited by 3
  9. 2026Disclosure By Design: Identity Transparency as a Behavioural Property of Conversational AI Modelscited by 3
  10. 2026F-Actor: Controllable Conversational Behaviour in Full-Duplex Modelscited by 2
  11. 2026RealityTest: How People Probe AI Identity and Whether Models Disclose Itcited by 1
  12. 2026Breaking the Illusion of Identity in LLM Toolingcited by 0
  13. 2026What do Reward Models Memorize?cited by 0
  14. 2026Six misconceptions about large language models: A minimal model and diagnostic taxonomycited by 0
  15. 2026Expectation, Backlash, Recovery, and Excitement: How Model Releases Shape Reddit Perceptions of Conversational AI Systemscited by 0