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.

标准问答评估协议未考虑到答案的多粒度性质，与单一粒度的参考答案进行比较。本研究提出 GRANOLA QA 评估设置，用于多粒度答案的准确性和信息量评估。通过对现有数据集进行改进，建立了 GRANOLA-EQ 多粒度实体问题数据集，并在 GRANOLA-EQ 上评估了一系列解码方法，包括 DRAG 算法。实验证明，大型语言模型在标准解码下往往生成具体但错误答案，而 DRAG 算法在多粒度答案上平均提高了近 20 个百分点的准确性，对于罕见实体进一步提高。总体而言，这表明标准评估和解码方法可能严重低估了语言模型所包含的知识。