Large language models (LLMs) memorize a vast amount of factual knowledge,
exhibiting strong performance across diverse tasks and domains. However, it has
been observed that the performance diminishes when dealing with less-popular or
low-frequency concepts and entities, for example in domain specific
applications. The two prominent approaches to enhance the performance of LLMs
on low-frequent topics are: Retrieval Augmented Generation (RAG) and
fine-tuning (FT) over synthetic data. This paper explores and evaluates the
impact of RAG and FT on customizing LLMs in handling low-frequency entities on
question answering task. Our findings indicate that FT significantly boosts the
performance across entities of varying popularity, especially in the most and
least popular groups, while RAG surpasses other methods. Additionally, the
success of both RAG and FT approaches is amplified by advancements in retrieval
and data augmentation techniques. We release our data and code at
this https URL

通过检索增强生成（RAG）和精调（FT）方法来定制大型语言模型（LLM），以应对低频实体的问答任务，结果表明，FT 显著提升性能，尤其在最受欢迎和最不受欢迎的实体组中，而 RAG 优于其他方法。