mlsanityData to 7 Oct 2026

Federated Retrieval Augmented Generation for Multi-Product Question Answering

arXiv 2501.14998 ↗First posted 25 Jan 2025cs.CLcited by 10

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

Recent advancements in Large Language Models and Retrieval-Augmented Generation have boosted interest in domain-specific question-answering for enterprise products. However, AI Assistants often face challenges in multi-product QA settings, requiring accurate responses across diverse domains. Existing multi-domain RAG-QA approaches either query all domains indiscriminately, increasing computational costs and LLM hallucinations, or rely on rigid resource selection, which can limit search results. We introduce MKP-QA, a novel multi-product knowledge-augmented QA framework with probabilistic federated search across domains and relevant knowledge. This method enhances multi-domain search quality by aggregating query-domain and query-passage probabilistic relevance. To address the lack of suitable benchmarks for multi-product QAs, we also present new datasets focused on three Adobe products: Adobe Experience Platform, Target, and Customer Journey Analytics. Our experiments show that MKP-QA significantly boosts multi-product RAG-QA performance in terms of both retrieval accuracy and response quality.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 20 of its 23 references that are in the map. First posted on arXiv 25 Jan 2025.

Builds on

20 of 23 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024A Multi-Source Retrieval Question Answering Framework Based on RAG29 May
  2. 2024Retrieval Augmented Generation for Domain-specific Question Answering23 Apr

Other references · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  5. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  6. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  7. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  8. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
  9. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  10. 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 996
  11. 2023BloombergGPT: A Large Language Model for Financecited by 923
  12. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 316
6 more
  1. 2023TheoremQA: A Theorem-driven Question Answering datasetcited by 189
  2. 2023Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecturecited by 119
  3. 2024In-context Learning with Retrieved Demonstrations for Language Models: A Surveycited by 69
  4. 2020Stochastic Client Selection for Federated Learning with Volatile Clientscited by 38
  5. 2020Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbolscited by 36
  6. 2024ReSLLM: Large Language Models are Strong Resource Selectors for Federated Searchcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 7.

10 papers.

  1. 2025Federated Retrieval-Augmented Generation: A Systematic Mapping Studycited by 5
  2. 2025DFAMS: Dynamic-flow guided Federated Alignment based Multi-prototype Searchcited by 2
  3. 2026FedMosaic: Federated Retrieval-Augmented Generation via Parametric Adapterscited by 2
  4. 2026SCOUT-RAG: Scalable and Cost-Efficient Unifying Traversal for Agentic Graph-RAG over Distributed Domainscited by 2
  5. 2025Task-Agnostic Federation over Decentralized Data: Research Landscape and Visionscited by 0
  6. 2026HaS: Accelerating RAG through Homology-Aware Speculative Retrievalcited by 0
  7. 2026Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systemscited by 0
  8. 2026VikingRAG: Accurate and Token-efficient Retrieval-augmented Generation over Structured Documentscited by 0
  9. 2026Beyond Raw Context Transfer: Representation-based Federated Retrieval-Augmented Generationcited by 0
  10. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024KaPQA: Knowledge-Augmented Product Question-Answering
  2. 2025DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation
  3. 2025Graph-Enhanced Retrieval-Augmented Question Answering for E-Commerce Customer Support
  4. 2024Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned and Retrieval-Augmented Generation Models
  5. 2025Contextually Aware E-Commerce Product Question Answering using RAG
  6. 2026VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge
  7. 2024RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering
  8. 2025Evaluation of retrieval-based QA on QUEST-LOFT