  Recently embedding-based retrieval or dense retrieval have shown state of the
art results, compared with traditional sparse or bag-of-words based approaches.
This paper introduces a model-agnostic doc-level embedding framework through
large language model (LLM) augmentation. In addition, it also improves some
important components in the retrieval model training process, such as negative
sampling, loss function, etc. By implementing this LLM-augmented retrieval
framework, we have been able to significantly improve the effectiveness of
widely-used retriever models such as Bi-encoders (Contriever, DRAGON) and
late-interaction models (ColBERTv2), thereby achieving state-of-the-art results
on LoTTE datasets and BEIR datasets.
