  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: https://github.com/Emma1066/Retrieval-Augmented-IT-OpenNER
