Pretrained Large Language Models (LLMs) have gained significant attention for
addressing open-domain Question Answering (QA). While they exhibit high
accuracy in answering questions related to common knowledge, LLMs encounter
difficulties in learning about uncommon long-tail knowledge (tail entities).
Since manually constructing QA datasets demands substantial human resources,
the types of existing QA datasets are limited, leaving us with a scarcity of
datasets to study the performance of LLMs on tail entities. In this paper, we
propose an automatic approach to generate specialized QA datasets for tail
entities and present the associated research challenges. We conduct extensive
experiments by employing pretrained LLMs on our newly generated long-tail QA
datasets, comparing their performance with and without external resources
including Wikipedia and Wikidata knowledge graphs.

通过预训练大型语言模型解决普适领域问答中的常见知识和罕见长尾知识学习难题，我们提出了一种自动生成专门用于长尾知识的问答数据集的自动方法，并展示了相关的研究挑战。通过预训练语言模型，在我们新生成的长尾问答数据集上进行了详尽实验，比较了其使用维基百科和维基数据知识图等外部资源的性能。