Layered Query Retrieval: An Adaptive Framework for Retrieval-Augmented Generation in Complex Question Answering for Large Language Models
Open accessAbstract
Retrieval-augmented generation (RAG) addresses the problem of knowledge cutoff and overcomes the inherent limitations of pre-trained language models by retrieving relevant information in real time. However, challenges related to efficiency and accuracy persist in current RAG strategies. A key issue is how to select appropriate methods for user queries of varying complexity dynamically. This study introduces a novel adaptive retrieval-augmented generation framework termed Layered Query Retrieval (LQR). The LQR framework focuses on query complexity classification, retrieval strategies, and relevance analysis, utilizing a custom-built training dataset to train smaller models that aid the large language model (LLM) in efficiently retrieving relevant information. A central technique in LQR is a semantic rule-based approach to distinguish between different levels of multi-hop queries. The process begins by parsing the user’s query for keywords, followed by a keyword-based document retrieval. Subsequently, we employ a natural language inference (NLI) model to assess whether the retrieved document is relevant to the query. We validated our approach on multiple single-hop and multi-hop datasets, demonstrating significant improvements in both accuracy and efficiency compared to existing single-step, multi-step, and adaptive methods. Our method exhibits high accuracy and efficiency, particularly on the HotpotQA dataset, where it outperforms the Adaptive-RAG method by improving accuracy by 9.4% and the F1 score by 16.14%. The proposed approach carefully balances retrieval efficiency with the accuracy of the LLM’s responses.
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Current RAG models face challenges related to frequent knowledge updates.
“Furthermore, current RAG models face challenges related to frequent knowledge updates [2,11,21,22].”
From Enhancing Medical Question Answering with LLMs via a Hybrid Retrieval-Augmented Generation Framework (2026) · cites this paper as [21]
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Measuring and Narrowing the Compositionality Gap in Language Models
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Enhancing Medical Question Answering with LLMs via a Hybrid Retrieval-Augmented Generation Framework
Aljohani, B., & Alsanoosy, T. (2026) · Information · doi:10.3390/info17020133 · Cited by 3 · see citing works
- Authors
- Huang, J., Wang, M., Cui, Y., Liu, J., Chen, L., Wang, T., Li, H., & Wu, J.
- Affiliations
- Agricultural Information Institute (CN)
- Corresponding author
- Wang, Mo
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2024-11-27
- Volume
- 14
- Issue
- 23
- Pages
- 11014
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4404765644
- Cited by
- 17
- References
- 10
- Citations by year
- 2026: 8 · 2025: 9
- FWCI
- 2.52
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- the National Key Research and Development Program Project (2023YFD1600305, BAIC10-2024-E18, BFGJ2022006, CAAS-ASTIP-2024-AII)
- Beijing Smart Agriculture Innovation Consortium Project (2023YFD1600305, BAIC10-2024-E18, BFGJ2022006, CAAS-ASTIP-2024-AII)
- Project of Northern Agriculture and Livestock Husbandry Technical Innovation Center, Chinese Academy of Agricultural Sciences (2023YFD1600305, BAIC10-2024-E18, BFGJ2022006, CAAS-ASTIP-2024-AII)
- the Chinese Academy of Agricultural Sciences Science and Technology Innovation Project Grant (2023YFD1600305, BAIC10-2024-E18, BFGJ2022006, CAAS-ASTIP-2024-AII)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- retrieval-augmented generation, multi-hop question answering, natural language inference, relevance assessment, HotpotQA, knowledge cutoff, large language models
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