Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunking

• URL: http://arxiv.org/abs/2606.01240v1
• Date: Sun, 31 May 2026 13:42:36 GMT
• Title: Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunking
• Abstract summary: InSemRAG is a framework for a retrieval-augmented generation (RAG) system.<n>IAR implements a dynamic hybrid retrieval method that adaptively weights the retrieval channels based on the query intent.<n>SPC performs detection and reparation to the damaged evidence chunks to preserve the semantic integrity.<n>Our method achieves significant gains on multi-hop and evidence-sensitive tasks, with a 2.65-point improvement in F1 on HotPotQA and a 1.5-point increase in accuracy on FEVER.
• Score: 18.698122955613584
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The demand for powerful instruction following and reasoning capability of large language models (LLMs) has promoted rapid development of retrieval-augmented generation (RAG). The RAG system assists LLM generation by retrieving chunks of query-fit supplementary knowledge from an external database. Conventional RAG systems, however, suffer from information insufficiency due to two factors, which are intent-agnostic retrieval and information fragmentation. Our work proposes a RAG framework, termed InSemRAG, that addresses these challenges via an iterative retrieve-and-check mechanism with two supporting modules, an intention-aware retriever (IAR) and semantics-preserving chunking (SPC). IAR implements a dynamic hybrid retrieval method that adaptively weights the retrieval channels based on the query intent, while SPC performs detection and reparation to the damaged evidence chunks to preserve the semantic integrity. To alleviate the computational latency brought by our iterative mechanism, we leverage small language models (SLMs). Extensive experiments across several benchmark datasets consistently demonstrate the competitiveness of our method against recent state-of-the-art RAG mechanisms. Particularly, our method achieves significant gains on multi-hop and evidence-sensitive tasks, with a 2.65-point improvement in F1 on HotPotQA and a 1.5-point increase in accuracy on FEVER. Our method also achieves competitive performance to Multi-Hop RAG with 4.32$\times$ lower latency with the utilization of SLM.

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