  This paper introduces xRAG, an innovative context compression method tailored
for retrieval-augmented generation. xRAG reinterprets document embeddings in
dense retrieval--traditionally used solely for retrieval--as features from the
retrieval modality. By employing a modality fusion methodology, xRAG seamlessly
integrates these embeddings into the language model representation space,
effectively eliminating the need for their textual counterparts and achieving
an extreme compression rate. In xRAG, the only trainable component is the
modality bridge, while both the retriever and the language model remain frozen.
This design choice allows for the reuse of offline-constructed document
embeddings and preserves the plug-and-play nature of retrieval augmentation.
Experimental results demonstrate that xRAG achieves an average improvement of
over 10% across six knowledge-intensive tasks, adaptable to various language
model backbones, ranging from a dense 7B model to an 8x7B Mixture of Experts
configuration. xRAG not only significantly outperforms previous context
compression methods but also matches the performance of uncompressed models on
several datasets, while reducing overall FLOPs by a factor of 3.53. Our work
pioneers new directions in retrieval-augmented generation from the perspective
of multimodality fusion, and we hope it lays the foundation for future
efficient and scalable retrieval-augmented systems
