Efficient Retrieval-Augmented Generation via Query-Adaptive Reranking and Lightweight Large Language Model Distillation
Keywords:
retrieval-augmented generation; query-adaptive reranking; knowledge distillation; lightweight language models; information retrieval; system architecture; governance; sustainabilityAbstract
Retrieval-augmented generation has become a central architecture for knowledge-intensive language applications because it combines parametric language models with external evidence. Yet its practical value depends less on any single model component than on the system-level orchestration of retrieval, reranking, generation, latency control, and governance. This paper examines an efficient retrieval-augmented generation architecture built on query-adaptive reranking and lightweight large language model distillation. The central argument is that reranking should not be treated as a fixed and expensive stage applied uniformly to all queries. Instead, queries can be routed through adaptive policies that estimate ambiguity, domain specificity, temporal sensitivity, and risk before selecting the depth and type of reranking. Such adaptation improves relevance while controlling computational cost, especially when paired with distilled language models that preserve sufficient reasoning and instruction-following capacity for generation and judgment. The paper discusses architecture, infrastructure, deployment, robustness, fairness, sustainability, and policy implications. It also considers how distillation can reduce energy consumption and latency but may narrow model diversity and introduce artifacts inherited from teacher models. The analysis concludes that efficient retrieval-augmented generation is best understood as a socio-technical infrastructure requiring continuous evaluation, transparent data governance, and adaptive resource allocation rather than static optimization.
References
1. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.
2. Zhou, D., & Yi, X. (2026). DISTRIBUTED AI-ASSISTED RISK ASSESSMENT FOR ZERO-TRUST MICROSERVICES: A TAXONOMY AND CONCEPTUAL FRAMEWORK. AI and Data Science Journal, 3(2), 1-13.
3. Izacard, G., & Grave, E. (2021). Leveraging passage retrieval with generative models for open domain question answering. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (pp. 874–880). Association for Computational Linguistics.
4. Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., van den Driessche, G., Lespiau, J.-B., Damoc, B., Clark, A., de Las Casas, D., Guy, A., Menick, J., Ring, R., Dziugaite, K., ... Sifre, L. (2022). Improving language models by retrieving from trillions of tokens. In Proceedings of the 39th International Conference on Machine Learning (pp. 2206–2240). PMLR.
5. Guu, K., Lee, K., Tung, Z., Pasupat, P., & Chang, M.-W. (2020). Retrieval augmented language model pre-training. In Proceedings of the 37th International Conference on Machine Learning (pp. 3929–3938). PMLR.
6. Thakur, N., Reimers, N., Rücklé, A., Srivastava, A., & Gurevych, I. (2021). BEIR: A heterogeneous benchmark for zero-shot evaluation of information retrieval models. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track.
7. Nogueira, R., & Cho, K. (2019). Passage re-ranking with BERT. arXiv preprint arXiv:1901.04085.
8. Khattab, O., & Zaharia, M. (2020). ColBERT: Efficient and effective passage search via contextualized late interaction over BERT. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 39–48). Association for Computing Machinery.
9. Santhanam, K., Khattab, O., Saad-Falcon, J., Potts, C., & Zaharia, M. (2022). ColBERTv2: Effective and efficient retrieval via lightweight late interaction. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 3715–3734). Association for Computational Linguistics.
10. Sun, W., Yan, L., Ma, X., Wang, S., Ren, P., Chen, Z., Yin, D., & Ren, Z. (2023). Is ChatGPT good at search? Investigating large language models as re-ranking agents. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 14918–14937). Association for Computational Linguistics.
11. Pradeep, R., Sharifymoghaddam, S., & Lin, J. (2023). RankZephyr: Effective and robust zero-shot listwise reranking is a breeze! arXiv preprint arXiv:2312.02724.
12. Ma, X., Gong, Y., He, P., Zhao, H., & Duan, N. (2023). Query rewriting in retrieval-augmented large language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 5303–5315). Association for Computational Linguistics.
13. Gao, L., Ma, X., Lin, J., & Callan, J. (2023). Precise zero-shot dense retrieval without relevance labels. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1762–1777). Association for Computational Linguistics.
14. Jin, Can, et al. "Rankflow: A multi-role collaborative reranking workflow utilizing large language models." Companion Proceedings of the ACM on Web Conference 2025. 2025.
15. Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531.
16. Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108.
17. Jiao, X., Yin, Y., Shang, L., Jiang, X., Chen, X., Li, L., Wang, F., & Liu, Q. (2020). TinyBERT: Distilling BERT for natural language understanding. In Findings of the Association for Computational Linguistics: EMNLP 2020 (pp. 4163–4174). Association for Computational Linguistics.
18. Wang, W., Wei, F., Dong, L., Bao, H., Yang, N., & Zhou, M. (2020). MiniLM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. Advances in Neural Information Processing Systems, 33, 5776–5788.
19. Gou, J., Yu, B., Maybank, S. J., & Tao, D. (2021). Knowledge distillation: A survey. International Journal of Computer Vision, 129, 1789–1819.
20. Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., Grave, E., LeCun, Y., & Scialom, T. (2023). Augmented language models: A survey. Transactions on Machine Learning Research.
21. Asai, A., Wu, Z., Wang, Y., Sil, A., & Hajishirzi, H. (2024). Self-RAG: Learning to retrieve, generate, and critique through self-reflection. In The Twelfth International Conference on Learning Representations.
22. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). Association for Computing Machinery.
23. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
24. Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., ... Gabriel, I. (2022). Taxonomy of risks posed by language models. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 214–229). Association for Computing Machinery.
25. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3645–3650). Association for Computational Linguistics.
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