Self-reported archetypes and behavioral failures in Large Language Models
Every large language model (LLM) has behavioral traits and moral preferences that comprise its character. Whether by design or as an emergent property of training, these systems exhibit persistent dispositions that shape how they interact, comply, resist, and err, yet the structure of LLM character remains poorly understood. We map the self-reported personality archetypes of 22 LLMs spanning closed-source frontier systems (GPT-4.0-5.2, Grok-3/4, Gemini 2.5 Pro/Flash, Claude Sonnet 4.5/4.6) and open-source models (Llama, DeepSeek, OLMo, and Qwen series). Each model self-rated across 464 bipolar semantic-differential trait pairs, and the resulting profiles were projected into a six-dimensio...
| Authors | Tabia Tanzin Prama, Calla Glavin Beauregard, Christopher M. Danforth et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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