Enhancing Enterprise Retrieval-Augmented Generation Systems through Reinforcement Learning-Based Adaptive Retrieval
Keywords:
Retrieval-Augmented Generation, Adaptive Retrieval, Reinforcement Learning, Enterprise Systems, Deep Q-NetworkAbstract
This study suggests an adaptive retrieval framework based on reinforcement learning for analyzing enterprise Retrieval-Augmented Generation (RAG) systems. In information retrieval systems, Deep Q-Networks are used to optimize document ranking in order to determine a relevance and accuracy of retrieved documents. Experimental results show better performance than traditional BM25 and Dense Retrieval. The framework can effectively improve the accessibility of enterprise knowledge and the efficiency of enterprise systems, through continuous learning and optimized reward, providing the activity of the enterprise environment with robust and intelligent information retrieval capabilities.
Downloads
References
Ram, O., Levine, Y., Dalmedigos, I., Muhlgay, D., Shashua, A., Leyton-Brown, K. and Shoham, Y., 2023. In-context retrieval-augmented language models. Transactions of the Association for Computational Linguistics, 11, pp.1316-1331.
Menda, J.R., 2022. Grounded generation for enterprise knowledge: Automated documentation and knowledge extraction using GenAI agents. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 8(3), pp.857-866.
Guo, J., Cai, Y., Fan, Y., Sun, F., Zhang, R. and Cheng, X., 2022. Semantic models for the first-stage retrieval: A comprehensive review. ACM Transactions on Information Systems (TOIS), 40(4), pp.1-42.
Chen, W., Liu, Y., Wang, W., Bakker, E.M., Georgiou, T., Fieguth, P., Liu, L. and Lew, M.S., 2022. Deep learning for instance retrieval: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6), pp.7270-7292.
Tick, A., Toktosunova, A., Fallah, H., Toutouchi, Z. and Tadzhibaeva, Z., 2023. The impact of ChatGPT on learning in higher education–results of a pilot study. Practice and Theory in Systems of Education, 18(1), pp.31-50.
Chen, L., Tang, Z. and Yang, G.H., 2020, July. Balancing reinforcement learning training experiences in interactive information retrieval. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1525-1528).
Zhou, Y., Dou, Z. and Wen, J.R., 2023, December. Enhancing generative retrieval with reinforcement learning from relevance feedback. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 12481-12490).
Tang, X., Chen, Y., Li, X., Liu, J. and Ying, Z., 2019. A reinforcement learning approach to personalized learning recommendation systems. British Journal of Mathematical and Statistical Psychology, 72(1), pp.108-135.
Kang, X., Zhao, Y., Zhang, J. and Zong, C., 2020, November. Dynamic context selection for document-level neural machine translation via reinforcement learning. In Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP) (pp. 2242-2254).
Ahmad, S., Miskon, S., Alabdan, R. and Tlili, I., 2020. Towards sustainable textile and apparel industry: Exploring the role of business intelligence systems in the era of industry 4.0. Sustainability, 12(7), p.2632.
Lin, W. and Byrne, B., 2022, December. Retrieval augmented visual question answering with outside knowledge. In Proceedings of the 2022 conference on empirical methods in natural language processing (pp. 11238-11254).
Gao, Z., Fu, G., Ouyang, C., Tsutsui, S., Liu, X., Yang, J., Gessner, C., Foote, B., Wild, D., Ding, Y. and Yu, Q., 2019. edge2vec: Representation learning using edge semantics for biomedical knowledge discovery. BMC bioinformatics, 20(1), p.306.
Fu, H. and Oh, S., 2019. Quality assessment of answers with user-identified criteria and data-driven features in social Q&A. Information Processing & Management, 56(1), pp.14-28.
Wang, X., Macdonald, C., Tonellotto, N. and Ounis, I., 2023. ColBERT-PRF: Semantic pseudo-relevance feedback for dense passage and document retrieval. ACM Transactions on the Web, 17(1), pp.1-39.
Hassani, A., Medvedev, A., Delir Haghighi, P., Ling, S., Zaslavsky, A. and Prakash Jayaraman, P., 2019. Context definition and query language: Conceptual specification, implementation, and evaluation. Sensors, 19(6), p.1478.
Castagna, F., Centobelli, P., Cerchione, R., Esposito, E., Oropallo, E. and Passaro, R., 2020. Customer knowledge management in SMEs facing digital transformation. Sustainability, 12(9), p.3899.
Khattab, O., Potts, C. and Zaharia, M., 2021. Relevance-guided supervision for openqa with colbert. Transactions of the association for computational linguistics, 9, pp.929-944.
Abusweilem, M. and Abualoush, S., 2019. The impact of knowledge management process and business intelligence on organizational performance. Management science letters, 9(12), pp.2143-2156.
Gusenbauer, M. and Haddaway, N.R., 2020. Which academic search systems are suitable for systematic reviews or meta‐analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Research synthesis methods, 11(2), pp.181-217.
Kęsek, M., Bogacz, P. and Migza, M., 2019, January. The application of Lean Management and Six Sigma tools in global mining enterprises. In IOP Conference Series: Earth and Environmental Science (Vol. 214, No. 1, p. 012090). IOP Publishing.
Yuvaraj, N. and Kumar, M.S., 2023. Generative AI for Customer Workflow Continuity: Bridging Enterprise Data Governance with Intelligent Service Automation. American International Journal of Computer Science and Technology, 5(6), pp.38-53.
Guu, K., Lee, K., Tung, Z., Pasupat, P. and Chang, M., 2020, November. Retrieval augmented language model pre-training. In International conference on machine learning (pp. 3929-3938). PMLR.
Seçkin, M., Seçkin, A.Ç. and Coşkun, A., 2019. Production fault simulation and forecasting from time series data with machine learning in glove textile industry. Journal of Engineered Fibers and Fabrics, 14, p.1558925019883462.
Kayes, A.S.M., Kalaria, R., Sarker, I.H., Islam, M.S., Watters, P.A., Ng, A., Hammoudeh, M., Badsha, S. and Kumara, I., 2020. A survey of context-aware access control mechanisms for cloud and fog networks: Taxonomy and open research issues. Sensors, 20(9), p.2464.
Klockner, K. and Meredith, P., 2020. Measuring resilience potentials: a pilot program using the resilience assessment grid. Safety, 6(4), p.51.
Tabassum, A. and Patil, R.R., 2020. A survey on text pre-processing & feature extraction techniques in natural language processing. International Research Journal of Engineering and Technology (IRJET), 7(06), pp.4864-4867.
Aggarwal, A., Chauhan, A., Kumar, D., Mittal, M., Roy, S. and Kim, T.H., 2020. Video caption based searching using end-to-end dense captioning and sentence embeddings. Symmetry, 12(6), p.992.
Palangi, H., Deng, L., Shen, Y., Gao, J., He, X., Chen, J., Song, X. and Ward, R., 2016. Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval. IEEE/ACM transactions on audio, speech, and language processing, 24(4), pp.694-707.
Goyal, A., Friesen, A., Banino, A., Weber, T., Ke, N.R., Badia, A.P., Guez, A., Mirza, M., Humphreys, P.C., Konyushova, K. and Valko, M., 2022, June. Retrieval-augmented reinforcement learning. In International Conference on Machine Learning (pp. 7740-7765). PMLR.
Amin, S., Uddin, M.I., Alarood, A.A., Mashwani, W.K., Alzahrani, A. and Alzahrani, A.O., 2023. Smart E-learning framework for personalized adaptive learning and sequential path recommendations using reinforcement learning. IEEe Access, 11, pp.89769-89790.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Hari Krishna Pokala
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
