发表机构
Polytechnic University of Marche; University of Modena and Reggio Emilia（马尔凯理工大学; 摩德纳和雷焦艾米利亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MatRAG提出结合俄罗斯套娃表示学习的层次化检索框架，通过有向无环图组织文档簇并利用维度感知相似性，在降低索引和查询成本的同时提升多跳问答检索质量。
AI 中文摘要
检索增强生成（RAG）系统在多跳问答（QA）中必须在检索质量与计算成本之间取得平衡。这一成本在索引阶段产生，通过使用昂贵的知识图谱（KG）或大型语言模型（LLM）生成摘要，或在查询阶段通过迭代的LLM驱动的检索产生。为了在保持检索质量的同时降低成本，我们提出了MatRAG，一个将RAG系统与俄罗斯套娃表示学习（MRL）相结合的层次化框架。MatRAG通过将聚类结构的语义层次与MRL的嵌套结构对齐来解决这两种成本。具体而言，它将文档语料库组织成一个有向无环图（DAG），其中簇的粒度逐步变粗。每一层由较低的俄罗斯套娃维度索引。MatRAG将DAG的迭代、自顶向下遍历与实体驱动的机制配对，该机制控制跳跃预算并重新排序候选。我们在三个标准多跳问答基准上对MatRAG进行了评估，并与七个代表性基线进行了比较。MatRAG在检索质量方面优于其最强的竞争对手；此外，它通过避免知识图谱构建和基于LLM的摘要生成降低了索引成本，并通过维度感知的相似性降低了查询时间成本。
英文摘要
Retrieval-Augmented Generation (RAG) systems for multi-hop Question Answering (QA) must balance retrieval quality with computational cost. This cost is incurred during indexing time, through the use of expensive Knowledge Graphs (KGs) or Large Language Models (LLMs) to generate summaries, or during querying, through iterative LLM-driven retrieval. To reduce it while maintaining retrieval quality, we present MatRAG, a hierarchical framework that combines RAG systems with Matryoshka Representation Learning (MRL). MatRAG addresses both kinds of cost by aligning the semantic hierarchy of a clustering structure with the nested structure of MRL. Specifically, it organizes the corpus of documents into a Directed Acyclic Graph (DAG) of clusters with progressively coarser granularity. Each level is indexed by a lower Matryoshka dimension. MatRAG pairs an iterative, top-down traversal of the DAG with an entity-driven mechanism that controls the hop budget and re-ranks candidates. We evaluated MatRAG on three standard multi-hop QA benchmarks against seven representative baselines. MatRAG outperforms its strongest competitors in terms of retrieval quality; furthermore, it reduces indexing costs by avoiding KG construction and LLM-based summarization, and lowers query-time costs through dimension-aware similarity.
