Autoregressive LLM user-interaction optimization strategies based on the model's probabilistic properties

Autoregressive LLMs optimize next-token probability, so they often produce plausible text rather than verified truth—making hallucinations a built-in tendency.The paper suggests reducing this by “zeroing in” with iterative, narrowing prompts and by giving descriptive, observed facts as grounding.Overall, it argues you should treat an LLM as a reasoning tool that works best when you supply reliable context, not as an oracle for truth.