  Factual questions typically can be answered correctly at different levels of
granularity. For example, both ``August 4, 1961'' and ``1961'' are correct
answers to the question ``When was Barack Obama born?''. Standard question
answering (QA) evaluation protocols, however, do not explicitly take this into
account and compare a predicted answer against answers of a single granularity
level. In this work, we propose GRANOLA QA, a novel evaluation setting where a
predicted answer is evaluated in terms of accuracy and informativeness against
a set of multi-granularity answers. We present a simple methodology for
enriching existing datasets with multi-granularity answers, and create
GRANOLA-EQ, a multi-granularity version of the EntityQuestions dataset. We
evaluate a range of decoding methods on GRANOLA-EQ, including a new algorithm,
called Decoding with Response Aggregation (DRAG), that is geared towards
aligning the response granularity with the model's uncertainty. Our experiments
show that large language models with standard decoding tend to generate
specific answers, which are often incorrect. In contrast, when evaluated on
multi-granularity answers, DRAG yields a nearly 20 point increase in accuracy
on average, which further increases for rare entities. Overall, this reveals
that standard evaluation and decoding schemes may significantly underestimate
the knowledge encapsulated in LMs.
