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.

该研究论文介绍了一种基于模型无关的文档级嵌入框架，通过大型语言模型（LLM）增强，改进了检索模型训练过程中的一些重要组件，如负采样、损失函数等。通过实现这个 LLM 增强的检索框架，我们显著提高了广泛使用的检索模型（如 Bi-encoders 和 late-interaction models）的效果，并在 LoTTE 数据集和 BEIR 数据集上取得了最新的研究成果。