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Retrieval-based methods have been shown to be effective in NLP tasks viaintroducing external knowledge. However, the indexing and retrieving oflarge-scale corpora bring considerable computational cost. Surprisingly, wefound that REtrieving from the traINing datA (REINA) only can lead tosignificant gains on multiple NLG and NLU tasks. We retrieve the labeledtraining instances most similar to the input text and then concatenate themwith the input to feed into the model to generate the output. Experimentalresults show that this simple method can achieve significantly betterperformance on a variety of NLU and NLG tasks, including summarization, machinetranslation, language modeling, and question answering tasks. For instance, ourproposed method achieved state-of-the-art results on XSum, BigPatent, andCommonsenseQA. Our code is released, https://github.com/microsoft/REINA .

Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data