What do LLMs think when you don't tell them what to think about?

This post introduces a study on LLM behavior under near-unconstrained generation, using minimal, topic-neutral prompts. It reveals that different model families exhibit distinct topical preferences (e.g., GPT-OSS favors programming/math, Llama leans literary, DeepSeek produces religious content, Qwen generates multiple-choice questions). The study also quantifies depth differences in technical content and analyzes degenerate text outputs as signals for safety and privacy risks, noting model-specific quirks like Llama generating social media URLs. This work contributes to understanding LLM priors beyond task-specific prompting and offers insights for model auditing and safety analysis.