The strong capability of large language models (LLMs) has been applied to
information extraction (IE) through either retrieval augmented prompting or
instruction tuning (IT). However, the best way to incorporate information with
LLMs for IE remains an open question. In this paper, we explore Retrieval
Augmented Instruction Tuning (RA-IT) for IE, focusing on the task of open named
entity recognition (NER). Specifically, for each training sample, we retrieve
semantically similar examples from the training dataset as the context and
prepend them to the input of the original instruction. To evaluate our RA-IT
approach more thoroughly, we construct a Chinese IT dataset for open NER and
evaluate RA-IT in both English and Chinese scenarios. Experimental results
verify the effectiveness of RA-IT across various data sizes and in both English
and Chinese scenarios. We also conduct thorough studies to explore the impacts
of various retrieval strategies in the proposed RA-IT framework. Code and data
are available at: this https URL

探索了检索增强的指示调节（RA-IT）方法在信息抽取领域的应用，特别关注开放式命名实体识别任务，通过从训练数据集中检索语义相似的例子作为上下文，并将其前置到原始指示的输入中，验证了 RA-IT 方法在不同数据规模和中英文情景下的有效性。