Retrieval Augmented Generation (RAG) system is important in domains such as e-commerce, which has many long-tail entities and frequently updated information. Most existing works adopt separate modules for retrieval and generation, which may be suboptimal since the retrieval task and the generation task cannot benefit from each other to improve performance. We propose a novel Backbone Shared RAG framework (BSharedRAG). It first uses a domain-specific corpus to continually pre-train a base model as a domain-specific backbone model and then trains two plug-and-play Low-Rank Adaptation (LoRA) modules based on the shared backbone to minimize retrieval and generation losses respectively. Experimental results indicate that our proposed BSharedRAG outperforms baseline models by 5% and 13% in Hit@3 upon two datasets in retrieval evaluation and by 23% in terms of BLEU-3 in generation evaluation. Our codes, models, and dataset are available at https://bsharedrag.github.io.

本研究针对电商领域信息更新频繁和长尾实体众多的问题，提出了一种新的共享检索增强生成框架BSharedRAG。该方法通过共享基础模型并结合低秩自适应模块，显著提升了检索和生成的效果，实验结果显示其在检索和生成评估中均超过了基线模型，具有重要的应用前景。

共享检索增强生成的电商领域基础框架（BSharedRAG）