提出联邦检索增强生成框架，提升多产品问答准确率
Federated Retrieval Augmented Generation for Multi-Product Question Answering
- 采用概率联邦搜索，在多个产品域间动态聚合相关性
- 在三个Adobe产品数据集上，检索准确率与回答质量显著提升
- 适合需要跨产品知识库的智能客服系统开发者
大语言模型与检索增强生成技术的发展推动了企业级产品的领域特定问答研究。然而，AI助手在多产品问答场景中常面临跨领域响应不准的问题。现有方法或盲目查询所有领域，增加计算开销和大模型幻觉；或依赖固定资源选择，限制搜索结果。本文提出MKP-QA，一种基于概率联邦搜索的多产品知识增强问答框架，通过融合查询-领域与查询-段落的双重概率相关性，提升跨域搜索质量。为填补多产品问答评估空白，我们构建了聚焦Adobe Experience Platform、Target和Customer Journey Analytics三个产品的全新数据集。实验表明，MKP-QA在检索准确率与回答质量上均显著优于基线方法。
原文摘要 · Abstract (English)
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.
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