LLMorphism: When humans come to see themselves as language models
Summary
The article discusses 'LLMorphism,' a concept where humans begin to view themselves through the lens of language models, exploring the implications for human cognition and self-perception.
View Cached Full Text
Cached at: 05/10/26, 12:42 PM
# LLMorphism: When humans come to see themselves as language models Source: [https://arxiv.org/abs/2605.05419](https://arxiv.org/abs/2605.05419) Bibliographic Tools ## Bibliographic and Citation Tools Bibliographic Explorer Toggle Code, Data, Media ## Code, Data and Media Associated with this Article Demos ## Demos Related Papers ## Recommenders and Search Tools About arXivLabs ## arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website\. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy\. arXiv is committed to these values and only works with partners that adhere to them\. Have an idea for a project that will add value for arXiv's community?[**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html)\.
Similar Articles
LLM Neuroanatomy III - LLMs seem to think in geometry, not language
Researcher analyzes LLM internal representations across 8 languages and multiple models, finding that concept thinking occurs in geometric space in middle transformer layers independent of input language, supporting a universal deep structure hypothesis similar to Chomsky's theory rather than Sapir-Whorf linguistic relativism.
HumanLLM: Benchmarking and Improving LLM Anthropomorphism via Human Cognitive Patterns
HumanLLM presents a framework for benchmarking and improving LLM anthropomorphism by modeling psychological patterns as interacting causal forces, constructing 244 patterns from academic literature and 11,359 multi-pattern scenarios. The approach demonstrates that authentic human alignment requires cognitive modeling rather than shallow behavioral mimicry, with HumanLLM-8B outperforming larger models like Qwen3-32B on multi-pattern dynamics.
Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, User Factors, and System Prompts
This paper presents a multi-dimensional analysis of human-like behaviors in LLMs, examining prevalence, effects, and controllability across 21,000 conversations from four models, finding that behaviors vary by model and user factors, with implications for responsible design.
What would optimal use of LLMs even look like?
Explores the speculative idea of optimizing human interaction with LLMs by conforming to their native communication patterns, such as using neuralese, rather than forcing them to adapt to human language.
Persona-Assigned Large Language Models Exhibit Human-Like Motivated Reasoning
This paper investigates whether assigning personas to large language models induces human-like motivated reasoning, finding that persona-assigned LLMs show up to 9% reduced veracity discernment and are up to 90% more likely to evaluate scientific evidence in ways congruent with their induced political identity, with prompt-based debiasing largely ineffective.