Can LLMs Take Retrieved Information with a Grain of Salt?

This paper evaluates the ability of large language models (LLMs) to adapt their responses to the certainty of retrieved information, revealing systematic limitations. It proposes an interaction strategy combining prior reminders, certainty recalibration, and context simplification to enhance LLM reliability. This approach reduces obedience errors by 25% without modifying model weights.