  Trustworthy language models should abstain from answering questions when they
do not know the answer. However, the answer to a question can be unknown for a
variety of reasons. Prior research has focused on the case in which the
question is clear and the answer is unambiguous but possibly unknown, but the
answer to a question can also be unclear due to uncertainty of the questioner's
intent or context. We investigate question answering from this perspective,
focusing on answering a subset of questions with a high degree of accuracy,
from a set of questions in which many are inherently ambiguous. In this
setting, we find that the most reliable approach to decide when to abstain
involves quantifying repetition within sampled model outputs, rather than the
model's likelihood or self-verification as used in prior work. We find this to
be the case across different types of uncertainty and model scales,and with or
without instruction tuning. Our results suggest that sampling-based confidence
scores help calibrate answers to relatively unambiguous questions, with more
dramatic improvements on ambiguous questions.
