What do LLMs think when you don't tell them what to think about?
Together AI
Researchers studied what large language models generate when given minimal, topic-neutral prompts without explicit instructions, finding that different model families exhibit distinct topical preferences independent of task specification. GPT-OSS defaults to programming (27.1%) and mathematics (24.6%), Llama produces more literary content, DeepSeek generates religious material at higher rates, and Qwen frequently outputs multiple-choice exam questions. The study reveals that model behaviors including depth of technical content and degenerate text patterns are systematic and model-specific rather than random, with implications for auditing, safety, and understanding unconstrained model behavior.
Why it matters
What do language models generate when you don't tell them what to generate? New research reveals that LLM families have distinct 'knowledge priors'—GPT models default to code and math, Llama favors narratives, DeepSeek generates religious content, and Qwen outputs exam questions.
