How do LLMs handle ambiguity in natural language queries?

They resolve ambiguity using context, probabilistic inference, and prompt conditioning.

Ambiguity is resolved by leveraging contextual embeddings and probabilistic token prediction. Models weigh multiple interpretations and select the most likely continuation. Retrieval augmentation can further disambiguate meaning using external knowledge.