mlsanityData to 7 Oct 2026

BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domain

arXiv 2409.20075 ↗First posted 30 Sep 2024cs.CLcited by 4

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 38 of its 52 references that are in the map. First posted on arXiv 30 Sep 2024.

Builds on

38 of 52 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Retrieval-Augmented Generation for AI-Generated Content: A Survey29 Feb
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 36

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24 more
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Cited by year

Citing papers per year: 2024: 1; 2025: 3.

4 papers.

  1. 2025Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systemscited by 125
  2. 2024OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Modelscited by 23
  3. 2025Neuro-Symbolic Query Compilercited by 4
  4. 2025Reference-Aligned Retrieval-Augmented Question Answering over Heterogeneous Proprietary Documentscited by 1

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