A Matryoshka Hierarchical RAG for Efficient Multi-Hop Question Answering
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 document...
| Authors | Gianluca Bonifazi, Christopher Buratti, Michele Marchetti et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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