  Uncertainty quantification enables users to assess the reliability of
responses generated by large language models (LLMs). We present a novel
Question Rephrasing technique to evaluate the input uncertainty of LLMs, which
refers to the uncertainty arising from equivalent variations of the inputs
provided to LLMs. This technique is integrated with sampling methods that
measure the output uncertainty of LLMs, thereby offering a more comprehensive
uncertainty assessment. We validated our approach on property prediction and
reaction prediction for molecular chemistry tasks.
