Abstract: The disclosure relates to a high-performance RAG-based answer generation method, which includes: acquiring a query; performing a first evaluation task based on the query using a pre-trained critique model; retrieving documents related to the query based on a result of the first evaluation task; performing a second evaluation task based on the query and the retrieved documents using the critique model; and generating one or more answers, based on the query and one or more related documents, using a large language model (LLM) according to a result of the second evaluation task.

Abstract: An apparatus for augmenting textual data according to an embodiment includes a data augmenter configured to generate augmented data by augmenting input textual data according to a data augmentation scheme decided based on a type of natural language processing task of the input textual data and a data classifier configured to classify the augmented data into a positive sample or a negative sample by determining whether or not the augmented data maintains label information of the input textual data based on one or more data classification criteria.

Abstract: An apparatus for augmenting textual data according to an embodiment includes a data augmenter configured to generate augmented data by augmenting input textual data according to a data augmentation scheme decided based on a type of natural language processing task of the input textual data and a data classifier configured to classify the augmented data into a positive sample or a negative sample by determining whether or not the augmented data maintains label information of the input textual data based on one or more data classification criteria.