  This study addresses a crucial challenge in instance-based relation
extraction using text generation models: end-to-end training in target relation
extraction task is not applicable to retrievers due to the non-differentiable
nature of instance selection. We propose a novel End-to-end TRAinable Soft
K-nearest neighbor retriever (ETRASK) by the neural prompting method that
utilizes a soft, differentiable selection of the $k$ nearest instances. This
approach enables the end-to-end training of retrievers in target tasks. On the
TACRED benchmark dataset with a low-resource setting where the training data
was reduced to 10\%, our method achieved a state-of-the-art F1 score of 71.5\%.
Moreover, ETRASK consistently improved the baseline model by adding instances
for all settings. These results highlight the efficacy of our approach in
enhancing relation extraction performance, especially in resource-constrained
environments. Our findings offer a promising direction for future research with
extraction and the broader application of text generation in natural language
processing.
