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

翻译：大型语言模型（LLM）的强大能力已通过检索增强提示或指令微调（IT）应用于信息抽取（IE）。然而，如何将信息与LLM结合以进行IE仍是一个开放性问题。本文探索了用于IE的检索增强指令微调（RA-IT），重点关注开放命名实体识别（NER）任务。具体而言，对于每个训练样本，我们从训练数据集中检索语义相似的示例作为上下文，并将其预置到原始指令的输入之前。为了更全面地评估我们的RA-IT方法，我们构建了一个用于开放NER的中文指令微调数据集，并在英文和中文场景下评估RA-IT。实验结果验证了RA-IT在不同数据规模及中英文场景下的有效性。我们还进行了深入研究，以探索所提出的RA-IT框架中各种检索策略的影响。代码与数据可在以下网址获取：https://github.com/Emma1066/Retrieval-Augmented-IT-OpenNER