  Relation Extraction (RE) aims at recognizing the relation between pairs of
entities mentioned in a text. Advances in LLMs have had a tremendous impact on
NLP. In this work, we propose a textual data augmentation framework called PGA
for improving the performance of models for RE in the scientific domain. The
framework introduces two ways of data augmentation, utilizing a LLM to obtain
pseudo-samples with the same sentence meaning but with different
representations and forms by paraphrasing the original training set samples. As
well as instructing LLM to generate sentences that implicitly contain
information about the corresponding labels based on the relation and entity of
the original training set samples. These two kinds of pseudo-samples
participate in the training of the RE model together with the original dataset,
respectively. The PGA framework in the experiment improves the F1 scores of the
three mainstream models for RE within the scientific domain. Also, using a LLM
to obtain samples can effectively reduce the cost of manually labeling data.
