# Believing Anthropomorphism: Examining the Role of Anthropomorphic Cues on Trust in Large Language Models

arXiv: https://arxiv.org/abs/2405.06079 · HTML: https://mlsanity.com/paper/2405.06079/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2024.

## Built on by (most cited first, 30 of 37)

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- [Humanlike AI Design Increases Anthropomorphism but Yields Divergent Outcomes on Engagement and Trust Globally](https://mlsanity.com/paper/2512.17898/) (2025)
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## Abstract (arXiv)

People now regularly interface with Large Language Models (LLMs) via speech and text (e.g., Bard) interfaces. However, little is known about the relationship between how users anthropomorphize an LLM system (i.e., ascribe human-like characteristics to a system) and how they trust the information the system provides. Participants (n=2,165; ranging in age from 18-90 from the United States) completed an online experiment, where they interacted with a pseudo-LLM that varied in modality (text only, speech + text) and grammatical person ("I" vs. "the system") in its responses. Results showed that the "speech + text" condition led to higher anthropomorphism of the system overall, as well as higher ratings of accuracy of the information the system provides. Additionally, the first-person pronoun ("I") led to higher information accuracy and reduced risk ratings, but only in one context. We discuss these findings for their implications for the design of responsible, human-generative AI experiences.