# Federated Retrieval Augmented Generation for Multi-Product Question Answering

arXiv: https://arxiv.org/abs/2501.14998 · HTML: https://mlsanity.com/paper/2501.14998/

## Key facts

- Cited by 10 papers in the map (data through 14 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.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP](https://mlsanity.com/paper/2212.14024/) (2022)
- [TheoremQA: A Theorem-driven Question Answering dataset](https://mlsanity.com/paper/2305.12524/) (2023)
- [Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecture](https://mlsanity.com/paper/2306.16092/) (2023)
- [In-context Learning with Retrieved Demonstrations for Language Models: A Survey](https://mlsanity.com/paper/2401.11624/) (2024)
- [Stochastic Client Selection for Federated Learning with Volatile Clients](https://mlsanity.com/paper/2011.08756/) (2020)
- [Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols](https://mlsanity.com/paper/2009.14237/) (2020)
- [Retrieval Augmented Generation for Domain-specific Question Answering](https://mlsanity.com/paper/2404.14760/) (2024)
- [ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search](https://mlsanity.com/paper/2401.17645/) (2024)
- [A Multi-Source Retrieval Question Answering Framework Based on RAG](https://mlsanity.com/paper/2405.19207/) (2024)

## Built on by (most cited first, 10 of 10)

- [Federated Retrieval-Augmented Generation: A Systematic Mapping Study](https://mlsanity.com/paper/2505.18906/) (2025)
- [DFAMS: Dynamic-flow guided Federated Alignment based Multi-prototype Search](https://mlsanity.com/paper/2508.20353/) (2025)
- [FedMosaic: Federated Retrieval-Augmented Generation via Parametric Adapters](https://mlsanity.com/paper/2602.05235/) (2026)
- [SCOUT-RAG: Scalable and Cost-Efficient Unifying Traversal for Agentic Graph-RAG over Distributed Domains](https://mlsanity.com/paper/2602.08400/) (2026)
- [Task-Agnostic Federation over Decentralized Data: Research Landscape and Visions](https://mlsanity.com/paper/2503.03140/) (2025)
- [HaS: Accelerating RAG through Homology-Aware Speculative Retrieval](https://mlsanity.com/paper/2604.20452/) (2026)
- [Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems](https://mlsanity.com/paper/2606.25533/) (2026)
- [VikingRAG: Accurate and Token-efficient Retrieval-augmented Generation over Structured Documents](https://mlsanity.com/paper/2609.11390/) (2026)
- [Beyond Raw Context Transfer: Representation-based Federated Retrieval-Augmented Generation](https://mlsanity.com/paper/2609.22162/) (2026)
- [Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning](https://mlsanity.com/paper/2610.01936/) (2026)

## 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.