A Modular Retrieval-Augmented Generation Architecture for Grounded Document Analysis
DOI:
https://doi.org/10.65310/k7qcxb59Keywords:
retrieval-augmented generation; grounded document analysis; claim-level attribution; hallucination verification; modular architecture; conceptual framework.Abstract
Organizations increasingly employ language models for the analysis of contracts, regulatory filings, technical manuals, and clinical guidance, where unsupported or misattributed statements create material reliability risks. Retrieval-augmented generation (RAG) mitigates some limitations of parametric models by conditioning generation on external evidence, but existing architectures vary substantially in retrieval, control, attribution, and verification mechanisms. Many implementations do not preserve sentence-level provenance or provide stage-level diagnostics for unsupported outputs. This study develops a conceptual RAG architecture using a design-oriented research method and a structured narrative synthesis of literature on retrieval, failure modes, mitigation, measurement, and governance. The synthesis yields eight requirements for grounded document analysis and informs a modular architecture comprising structure-aware segmentation, hybrid indexing, query planning, retrieval, reranking, provenance-preserving evidence assembly, citation-mandatory generation, and claim-level verification. The architecture specifies evidence-sufficiency and claim-verification gates, bounded repair, and single-pass, iterative, and abstaining execution policies. Its principal contribution is an explicit system specification that integrates span-level attribution, structural preservation, conflict transparency, calibrated abstention, and stage-level telemetry. Because no implementation or empirical benchmark is reported, the proposed mechanisms are treated as design propositions. Seven testable hypotheses and a four-phase evaluation protocol are specified for subsequent empirical assessment.
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Adebayo, A., Adegbite, M. P., & Ahmed, M. O. (2022). Adversarial machine learning in critical infrastructure: A conceptual framework for threat modeling AI enabled OT systems. World Journal of Innovation and Modern Technology, 6(1), 184–234. https://doi.org/10.56201/wjimt.v6.no1.2022.pg184.234
Adegbite, M. P., Adebayo, A., & Ahmed, M. O. (2024). A vulnerability governance architecture for power and utilities corporations: From exposure mapping to remediation verification. World Journal of Innovation and Modern Technology, 8(6), 185–246.
Adegbite, M. P., Ahmed, M. O., & Adebayo, A. (2026). Securing generative AI pipelines in critical infrastructure environments: A conceptual architecture for identity, data integrity, and adversarial risk. Advances in IT and Digital Security, 1(2), 1–46.
Adelanwa, A., Basnet, A., & Anene, U. N. (2024). Advanced AI based decision support systems for healthcare operations and resource planning. Gyanshauryam, International Scientific Refereed Research Journal, 7(1), 244–276.
Afrihyia, E., Akinse, S. G., & Ojukwu, P. U. (2024). Comparative governance of AI-driven healthcare management: Executive oversight, regulatory structures, and accountability in the United States and developing countries. International Journal of Advanced Multidisciplinary Research and Studies, 4(6), 3087–3102. https://doi.org/10.62225/2583049X.2024.4.6.5920
Afrihyia, E., Akinse, S. G., & Ojukwu, P. U. (2025). Organizational readiness for generative AI integration in healthcare operations: Comparative management capabilities between the U.S. and low- and middle-income countries. Iconic Research and Engineering Journals, 8(10), 1673–1697. https://doi.org/10.64388/IREV8I10-1714670
Agu, M. U., Akomolafe, O., & Bello, A. (2023). A comparative review of SOX compliance frameworks in cross-border financial auditing. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2297–2306. https://doi.org/10.62225/2583049X.2023.3.6.5362
Ahmed, M. O., Adegbite, M. P., & Adebayo, A. (2021). Zero trust architecture for operational technology in North American critical infrastructure: A framework for implementation and resilience optimization. International Journal of Engineering and Modern Technology, 7(1), 66–113.
Akintola, A. S., Fawehinmi, Y. O., Aiyenitaju, O., Chinemerem, B., & Emmanuella, O. (2025). Digital transformation in telehealth: A systematic review of user trust, privacy, and regulatory governance in AI-powered remote monitoring systems. Journal of Scientific Research and Reports, 31(11), 163–182. https://doi.org/10.9734/jsrr/2025/v31i113658
Akomolafe, O., & Agu, M. U. (2018). A conceptual model for enhancing internal audit quality through technology-enabled risk assessment frameworks. Iconic Research and Engineering Journals, 1(9), 458–475.
Akomolafe, O., Agu, M. U., & Bello, A. (2025). A conceptual model for advancing risk governance through data-driven compliance analytics in financial institutions. Journal of Accounting and Financial Management, 11(11), 211–228. https://doi.org/10.56201/jafm.vol.11.no11.2025.pg211.228
Aliliele, C., Mbonu, I. S., & Iwuanyanwu, U. (2023). A review of API governance and risk prioritization frameworks in modern financial institutions. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 9(10), 395–433. https://doi.org/10.32628/CSEIT2361072
Aliliele, C., Mbonu, I. S., & Iwuanyanwu, U. (2024). A conceptual framework for enterprise data sensitivity classification and regulatory traceability mechanisms. International Journal of Advanced Multidisciplinary Research and Studies, 4(6), 3103–3124. https://doi.org/10.62225/2583049X.2024.4.6.5991
Aliliele, C., Mbonu, I. S., Uzoka, E., & Iwuanyanwu, U. (2025a). A review of AI assisted continuous auditing systems in technology risk and cybersecurity oversight. Gyanshauryam, International Scientific Refereed Research Journal, 8(4), 210–250. https://doi.org/10.32628/GISRRJ258369
Aliliele, C., Mbonu, I. S., Uzoka, E., & Iwuanyanwu, U. (2025b). Advances in data lakehouse governance architectures for enterprise data loss prevention and compliance assurance. Shodhshauryam, International Scientific Refereed Research Journal, 8(4), 171–213. https://doi.org/10.32628/SHISRRJ258474
Aneke, O. B., & Adesemoye, A. C. (2019). Toward an integrated conceptual framework for traceability and chain-of-custody integrity in pharmaceutical distribution. Iconic Research and Engineering Journals, 3(4), 655–672.
Aneke, O. B., Adesemoye, A. C., & Sydney, E. (2022). Corrective and preventive action effectiveness in pharmaceutical quality management systems: A systematic review of root-cause methodologies. International Journal of Health and Pharmaceutical Research, 7(2), 78–111.
Aneke, O. B., Adesemoye, A. C., & Sydney, E. (2024). Regulatory harmonization and convergence in global medicine quality assurance: A review of frameworks, pathways, and implementation barriers. International Journal of Medical Evaluation and Physical Report, 8(6), 213–252.
Aneke, O. B., Sydney, E., & Adesemoye, A. C. (2023). Data integrity and governance in regulated life sciences operations: A review of failures, controls, and remediation strategies. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 9(10), 538–568.
Annan, A. O. (2022). Data privacy governance models for cross-border digital platforms. International Journal of Multidisciplinary Research and Growth Evaluation, 3(6), 949–964.
Annan, A. O. (2023). Intellectual property ownership and authorship in AI-generated works. Gyanshauryam, International Scientific Refereed Research Journal, 6(3), 425–450.
Annan, A. O. (2024). Algorithmic accountability and trade secret protection in artificial intelligence. Shodhshauryam, International Scientific Refereed Research Journal, 7(5), 315–347.
Annan, A. O. (2025). Automated decision-making and anti-discrimination compliance under U.S. law. International Journal of Advanced Multidisciplinary Research and Studies, 5(2), 2522–2540.
Asai, A., Wu, Z., Wang, Y., Sil, A., & Hajishirzi, H. (2024). Self-RAG: Learning to retrieve, generate, and critique through self-reflection. Proceedings of the International Conference on Learning Representations.
Badmus, O., Dosunmu, A. A., & Anunagba, C. O. (2025). A governance framework for AI-assisted CRM workflows: Agentforce deployment, risk management, and organizational readiness in enterprise Salesforce environments. International Journal of Scientific Research in Humanities and Social Sciences, 2(1), 59–80.
Badmus, O., Jooda, D., & Anunagba, C. O. (2024). A privacy-by-design framework for role-based security architecture in Salesforce environments handling sensitive personal data. International Journal of Advanced Multidisciplinary Research and Studies, 4(6), 3244–3254. https://doi.org/10.62225/2583049X.2024.4.6.6243
Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., & Abdelrazek, M. (2024). Seven failure points when engineering a retrieval augmented generation system. Proceedings of the IEEE/ACM International Conference on AI Engineering, 194–199. https://doi.org/10.1145/3644815.3644945
Borah, S., Aliliele, K. C., Rakshit, S., & Vajjhala, N. R. (2022). Applications of artificial intelligence in software testing. In P. K. Mallick, A. K. Bhoi, P. Barsocchi, & V. H. C. de Albuquerque (Eds.), Cognitive informatics and soft computing (Lecture Notes in Networks and Systems, Vol. 375, pp. 727–736). Springer. https://doi.org/10.1007/978-981-16-8763-1_60
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., Hennigan, T., Huang, S., Maggiore, L., Jones, C., Cassirer, A., … Sifre, L. (2022). Improving language models by retrieving from trillions of tokens. Proceedings of the International Conference on Machine Learning, 2206–2240.
Chen, D., Fisch, A., Weston, J., & Bordes, A. (2017). Reading Wikipedia to answer open-domain questions. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 1870–1879. https://doi.org/10.18653/v1/P17-1171
Chen, J., Lin, H., Han, X., & Sun, L. (2024). Benchmarking large language models in retrieval-augmented generation. Proceedings of the AAAI Conference on Artificial Intelligence, 38(16), 17754–17762. https://doi.org/10.1609/aaai.v38i16.29728
Dahl, M., Magesh, V., Suzgun, M., & Ho, D. E. (2024). Large legal fictions: Profiling legal hallucinations in large language models. Journal of Legal Analysis, 16(1), 64–93. https://doi.org/10.1093/jla/laae003
Dhuliawala, S., Komeili, M., Xu, J., Raileanu, R., Li, X., Celikyilmaz, A., & Weston, J. (2024). Chain-of-verification reduces hallucination in large language models. Findings of the Association for Computational Linguistics: ACL 2024, 3563–3578. https://doi.org/10.18653/v1/2024.findings-acl.212
Dosunmu, A. A., & Ogundele, P. O. (2024a). Breach and attack simulation frameworks for continuous validation of enterprise security controls. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(3), 1100–1119.
Dosunmu, A. A., & Ogundele, P. O. (2024b). Threat informed defence engineering models for measuring security control effectiveness at scale. International Journal of Advanced Multidisciplinary Research and Studies, 4(6), 2847–2858.
Dosunmu, A. A., & Ogundele, P. O. (2025a). Cyber defense performance measurement frameworks for executive and board level governance. Computer Science and IT Research Journal, 6(11), 895–913. https://doi.org/10.51594/csitrj.v6i11.2164
Dosunmu, A. A., & Ogundele, P. O. (2025b). Security orchestration and automation models for accelerating incident detection and response. Computer Science and IT Research Journal, 6(11), 878–894. https://doi.org/10.51594/csitrj.v6i11.2163
Ebhojie, O., Dogbatsey, E. A., & Oyeleye, A. O. (2023). Audit liaison, corrective action planning, and control compliance in multinational organisations: A systematic literature review. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2839–2850. https://doi.org/10.62225/2583049X.2023.3.6.6200
Edge, D., Trinh, H., Cheng, N., Bradley, J., Chao, A., Mody, A., Truitt, S., Metropolitansky, D., Ness, R. O., & Larson, J. (2024). From local to global: A graph RAG approach to query-focused summarization. arXiv. https://doi.org/10.48550/arXiv.2404.16130
Edivri, J., & Oteri, O. (2024). A conceptual KPI-driven decision and optimization framework for IT service delivery, portfolio performance, and adoption. Gyanshauryam, International Scientific Refereed Research Journal, 7(1), 167–187.
Ekechi, N. V., Ozowara, D. E., & Anunagba, C. O. (2026). Conceptual framework for AI governance, data privacy compliance, and financial sustainability in digital health. Computer Science and IT Research Journal, 7(4), 275–299.
Es, S., James, J., Espinosa Anke, L., & Schockaert, S. (2024). RAGAS: Automated evaluation of retrieval augmented generation. Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, 150–158. https://doi.org/10.18653/v1/2024.eacl-demo.16
Eze, F. I., Akinleye, O. K., & Anene, U. N. (2024). Development of a risk-based accelerated approval decision model balancing speed, safety, and evidence quality in pharmaceutical regulatory science. Research Journal of Pure Science and Technology, 7(6), 120–161. https://doi.org/10.56201/rjpst.v7.no6.2024.pg120.161
Farquhar, S., Kossen, J., Kuhn, L., & Gal, Y. (2024). Detecting hallucinations in large language models using semantic entropy. Nature, 630(8017), 625–630. https://doi.org/10.1038/s41586-024-07421-0
Gao, L., Dai, Z., Pasupat, P., Chen, A., Chaganty, A. T., Fan, Y., Zhao, V. Y., Lao, N., Lee, H., Juan, D.-C., & Guu, K. (2023). RARR: Researching and revising what language models say, using language models. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 16477–16508. https://doi.org/10.18653/v1/2023.acl-long.910
Gao, T., Yen, H., Yu, J., & Chen, D. (2023). Enabling large language models to generate text with citations. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 6465–6488. https://doi.org/10.18653/v1/2023.emnlp-main.398
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Wang, M., & Wang, H. (2024a). Retrieval-augmented generation for large language models: A survey. arXiv. https://doi.org/10.48550/arXiv.2312.10997
Gao, Y., Xiong, Y., Wang, M., & Wang, H. (2024b). Modular RAG: Transforming RAG systems into LEGO-like reconfigurable frameworks. arXiv. https://doi.org/10.48550/arXiv.2407.21059
Gou, Z., Shao, Z., Gong, Y., Shen, Y., Yang, Y., Duan, N., & Chen, W. (2024). CRITIC: Large language models can self-correct with tool-interactive critiquing. Proceedings of the International Conference on Learning Representations.
Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. Proceedings of the International Conference on Machine Learning, 1321–1330.
Guu, K., Lee, K., Tung, Z., Pasupat, P., & Chang, M.-W. (2020). Retrieval augmented language model pre-training. Proceedings of the International Conference on Machine Learning, 3929–3938.
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105. https://doi.org/10.2307/25148625
Honovich, O., Aharoni, R., Herzig, J., Taitelbaum, H., Kukliansy, D., Cohen, V., Scialom, T., Szpektor, I., Hassidim, A., & Matias, Y. (2022). TRUE: Re-evaluating factual consistency evaluation. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics, 3905–3920. https://doi.org/10.18653/v1/2022.naacl-main.287
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., & Liu, T. (2025). A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Transactions on Information Systems, 43(2), Article 42. https://doi.org/10.1145/3703155
Izacard, G., & Grave, E. (2021). Leveraging passage retrieval with generative models for open domain question answering. Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics, 874–880. https://doi.org/10.18653/v1/2021.eacl-main.74
Jaakkola, E. (2020). Designing conceptual articles: Four approaches. AMS Review, 10(1–2), 18–26. https://doi.org/10.1007/s13162-020-00161-0
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), Article 248. https://doi.org/10.1145/3571730
Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., & Neubig, G. (2023). Active retrieval augmented generation. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 7969–7992. https://doi.org/10.18653/v1/2023.emnlp-main.495
Johnson, J., Douze, M., & Jégou, H. (2021). Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, 7(3), 535–547. https://doi.org/10.1109/TBDATA.2019.2921572
Jones, B. C., & Ominyi, M. (2023). Reviewing compliance frameworks for technology transfers between developed and emerging markets in an era of export controls and sanctions. World Journal of Innovation and Modern Technology, 7(2), 121–149.
Jones, B. C., & Ominyi, M. (2024). Applications of artificial intelligence in customs administration and trade facilitation: A review. World Journal of Innovation and Modern Technology, 8(6), 181–217. https://doi.org/10.56201/wjimt.v8.no6.2024.pg181.217
Jones, B. C., & Ominyi, M. (2025). Conceptualizing AI-driven advisory services for expanding trade compliance access to small and medium enterprises. IIARD International Journal of Economics and Business Management, 11(12), 441–474. https://doi.org/10.56201/ijebm.vol.11.no12.2025.pg441.474
Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., Schiefer, N., Hatfield-Dodds, Z., DasSarma, N., Tran-Johnson, E., Johnston, S., El-Showk, S., Jones, A., Elhage, N., Hume, T., Chen, A., Bai, Y., Bowman, S., Fort, S., … Kaplan, J. (2022). Language models (mostly) know what they know. arXiv. https://doi.org/10.48550/arXiv.2207.05221
Kandpal, N., Deng, H., Roberts, A., Wallace, E., & Raffel, C. (2023). Large language models struggle to learn long-tail knowledge. Proceedings of the International Conference on Machine Learning, 15696–15707.
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-t. (2020). Dense passage retrieval for open-domain question answering. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 6769–6781. https://doi.org/10.18653/v1/2020.emnlp-main.550
Khattab, O., & Zaharia, M. (2020). ColBERT: Efficient and effective passage search via contextualized late interaction over BERT. Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, 39–48. https://doi.org/10.1145/3397271.3401075
Kryscinski, W., McCann, B., Xiong, C., & Socher, R. (2020). Evaluating the factual consistency of abstractive text summarization. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 9332–9346. https://doi.org/10.18653/v1/2020.emnlp-main.750
Kuhn, L., Gal, Y., & Farquhar, S. (2023). Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation. Proceedings of the International Conference on Learning Representations.
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., Toutanova, K., Jones, L., Kelcey, M., Chang, M.-W., Dai, A. M., Uszkoreit, J., Le, Q., & Petrov, S. (2019). Natural Questions: A benchmark for question answering research. Transactions of the Association for Computational Linguistics, 7, 452–466. https://doi.org/10.1162/tacl_a_00276
Laban, P., Schnabel, T., Bennett, P. N., & Hearst, M. A. (2022). SummaC: Re-visiting NLI-based models for inconsistency detection in summarization. Transactions of the Association for Computational Linguistics, 10, 163–177. https://doi.org/10.1162/tacl_a_00453
Ladapo, O. O., Dosunmu, A. A., Jooda, D., & Abolaji, T. O. (2022). Human-in-the-loop machine learning: A state of the art. Journal of Frontiers in Multidisciplinary Research, 3(1), 656–669. https://doi.org/10.54660/.JFMR.2022.3.1.656-669
Ladapo, O. O., Dosunmu, A. A., Jooda, D., & Abolaji, T. O. (2023). An IT service management literature review: Challenges, benefits, opportunities, and implementation practices. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2875–2889. https://doi.org/10.62225/2583049X.2023.3.6.6205
Ladapo, O. O., Dosunmu, A. A., Jooda, D., & Abolaji, T. O. (2024). Integrated network and security operation center: A systematic analysis. International Journal of Multidisciplinary Futuristic Development, 5(1), 65–80. https://doi.org/10.54660/IJMFD.2024.5.1.65-80
Ladapo, O. O., Jooda, D., Dosunmu, A. A., & Abolaji, T. O. (2024). Keeping humans in the loop: Human-centered automated annotation with generative AI. International Journal of Multidisciplinary Futuristic Development, 5(1), 81–95. https://doi.org/10.54660/IJMFD.2024.5.1.81-95
Ladapo, O. O., Jooda, D., Dosunmu, A. A., & Abolaji, T. O. (2026). On the disagreement problem in human-in-the-loop federated machine learning. International Journal of Multidisciplinary Research and Growth Evaluation, 7(3), 178–192. https://doi.org/10.54660/.IJMRGE.2026.7.3.178-192
Lazaridou, A., Kuncoro, A., Gribovskaya, E., Agrawal, D., Liska, A., Terzi, T., Gimenez, M., de Masson d'Autume, C., Kocisky, T., Ruder, S., Yogatama, D., Cao, K., Young, S., & Blunsom, P. (2021). Mind the gap: Assessing temporal generalization in neural language models. Advances in Neural Information Processing Systems, 34, 29348–29363.
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.
Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., & Liang, P. (2024). Lost in the middle: How language models use long contexts. Transactions of the Association for Computational Linguistics, 12, 157–173. https://doi.org/10.1162/tacl_a_00638
Longpre, S., Perisetla, K., Chen, A., Ramesh, N., DuBois, C., & Singh, S. (2021). Entity-based knowledge conflicts in question answering. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 7052–7063. https://doi.org/10.18653/v1/2021.emnlp-main.565
Ma, X., Gong, Y., He, P., Zhao, H., & Duan, N. (2023). Query rewriting in retrieval-augmented large language models. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 5303–5315. https://doi.org/10.18653/v1/2023.emnlp-main.322
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., Gupta, S., Majumder, B. P., Hermann, K., Welleck, S., Yazdanbakhsh, A., & Clark, P. (2023). Self-Refine: Iterative refinement with self-feedback. Advances in Neural Information Processing Systems, 36, 46534–46594.
Malkov, Y. A., & Yashunin, D. A. (2020). Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(4), 824–836. https://doi.org/10.1109/TPAMI.2018.2889473
Mallen, A., Asai, A., Zhong, V., Das, R., Khashabi, D., & Hajishirzi, H. (2023). When not to trust language models: Investigating effectiveness of parametric and non-parametric memories. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 9802–9822. https://doi.org/10.18653/v1/2023.acl-long.546
Manakul, P., Liusie, A., & Gales, M. J. F. (2023). SelfCheckGPT: Zero-resource black-box hallucination detection for generative large language models. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 9004–9017. https://doi.org/10.18653/v1/2023.emnlp-main.557
Mbonu, I. S., Aliliele, C., Iwuanyanwu, U., & Oluoha, O. M. (2018). A conceptual framework for legal and ethical risk modeling in enterprise data protection governance systems. Iconic Research and Engineering Journals, 2(2), 207–226. https://doi.org/10.64388/IREV2I2-1714911
Mbonu, I. S., Aliliele, C., Iwuanyanwu, U., & Uzoka, E. (2021). Advances in artificial intelligence techniques for secure software testing and automated regression control mechanisms. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 7(5), 468–496. https://doi.org/10.32628/CSEIT217565
Mbonu, I. S., Aliliele, C., Iwuanyanwu, U., & Uzoka, E. (2022). A conceptual framework for AI enabled IT general controls and SOX audit automation processes. Gyanshauryam, International Scientific Refereed Research Journal, 5(5), 384–414. https://doi.org/10.32628/GISRRJ2256239
Mbonu, I. S., Iwuanyanwu, U., Aliliele, C., & Uzoka, E. (2020). Advances in infrastructure as code governance for secure terraform based enterprise cloud deployments. International Journal of Multidisciplinary Research and Growth Evaluation, 1(5), 811–828. https://doi.org/10.54660/.IJMRGE.2020.1.5.811-828
Mbonu, I. S., Iwuanyanwu, U., Aliliele, C., & Uzoka, E. (2022). A review of data protection impact assessment models in multi cloud financial infrastructure systems. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 8(1), 589–623. https://doi.org/10.32628/CSEIT25442
Mbonu, I. S., Iwuanyanwu, U., Uzoka, E., & Oluoha, O. M. (2019). Advances in enterprise log analytics and automated incident response architectures using Python and SIEM platforms. Iconic Research and Engineering Journals, 3(2), 1000–1019. https://doi.org/10.64388/IREV3I2-1714915
Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W.-t., Koh, P. W., Iyyer, M., Zettlemoyer, L., & Hajishirzi, H. (2023). FActScore: Fine-grained atomic evaluation of factual precision in long form text generation. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 12076–12100. https://doi.org/10.18653/v1/2023.emnlp-main.741
Nogueira, R., & Cho, K. (2019). Passage re-ranking with BERT. arXiv. https://doi.org/10.48550/arXiv.1901.04085
Nwakamma, S., Ojukwu, J., & Oyesiji, S. O. (2024a). LLM-as-a-judge for automated model governance: A review of methods, biases, and release-gating practices. World Journal of Innovation and Modern Technology, 8(6), 185–216. https://doi.org/10.56201/wjimt.v8.no6.2024.pg185.216
Nwakamma, S., Ojukwu, J., & Oyesiji, S. O. (2024b). Securing agentic AI enterprise workflows against prompt injection, tool poisoning, memory manipulation, and excessive agency threats. World Journal of Innovation and Modern Technology, 8(6), 217–248. https://doi.org/10.56201/wjimt.v8.no6.2024.pg217.248
Nwakamma, S., Ojukwu, J., & Oyesiji, S. O. (2025). On-device multimodal small language models for privacy-preserving edge intelligence through quantization, pruning, distillation, and energy-efficient inference. World Journal of Innovation and Modern Technology, 9(12), 271–302. https://doi.org/10.56201/wjimt.v9.no12.2025.pg271.302
Odejobi, O. D., Okonkwo, C. S., Ahiaeke Patrick, M. C., Okeke, O. T., & Mayo, W. (2025). AI-augmented secure software engineering: Leveraging deep learning for autonomous threat detection and mitigation. International Journal of Engineering and Modern Technology, 11(12), 101–121.
Ogbole, J. I., Okoruwa, P. O., Fadayomi, O., Akeju, B., Edivri, J., & Abolaji, T. O. (2025). Security analytics and digital forensics for enterprise risk management, advances and practical implications. International Journal of Advanced Multidisciplinary Research and Studies, 5(6), 2017–2028.
Ojukwu, J., Oyesiji, S. O., & Nwakamma, S. (2025). WebAssembly component model and WASI for portable cloud-to-edge software: Language interoperability, security, performance, and container comparisons. International Journal of Computer Science and Mathematical Theory, 11(12), 232–263. https://doi.org/10.56201/ijcsmt.vol.11.no12.2025.pg232.263
Ominyi, M., & Anichukwueze, C. C. (2021). A systematic review of anti-money laundering and anti-bribery compliance regimes across emerging and developed markets. International Journal of Economics and Financial Management, 6(2), 89–118. https://doi.org/10.56201/ijefm.v6.no2.2021.pg89.118
Ominyi, M., & Anichukwueze, C. C. (2022a). A conceptual framework for harmonizing data protection compliance across GDPR, NDPR, and United States privacy regimes. International Journal of Social Sciences and Management Research, 8(5), 148–174. https://doi.org/10.56201/ijssmr.v8.no5.2022.pg148.174
Ominyi, M., & Anichukwueze, C. C. (2022b). Reviewing greenhouse gas disclosure and ESG compliance obligations for energy and infrastructure enterprises under evolving regulatory regimes. International Journal of Economics and Financial Management, 7(6), 94–124. https://doi.org/10.56201/ijefm.v7.no6.2022.pg94.124
Ominyi, M., & Anichukwueze, C. C. (2023). A review of algorithmic accountability and model risk management approaches in financial services. Journal of Accounting and Financial Management, 9(12), 218–238. https://doi.org/10.56201/jafm.v9.no12.2023.pg218.238
Ominyi, M., & Anichukwueze, C. C. (2024). Conceptualizing responsible AI governance structures for automated decision systems in regulated industries. International Journal of Social Sciences and Management Research, 10(11), 403–430. https://doi.org/10.56201/ijssmr.v10.no11.2024.pg.403.430
Ominyi, M., Anichukwueze, C. C., & Uzougbo, N. S. (2024a). Developing a conceptual framework for AI-assisted compliance monitoring and anomaly detection in high-volume transaction environments. World Journal of Innovation and Modern Technology, 8(6), 204–228. https://doi.org/10.56201/wjimt.v8.no6.2024.pg204.228
Ominyi, M., Anichukwueze, C. C., & Uzougbo, N. S. (2024b). A systematic review of organizational readiness for the EU Artificial Intelligence Act in multinational enterprises. World Journal of Innovation and Modern Technology, 8(6), 185–203. https://doi.org/10.56201/wjimt.v8.no6.2024.pg185.203
Ominyi, M., Anichukwueze, C. C., & Uzougbo, N. S. (2025a). A conceptual framework for multi-agent AI quality control in the review of regulated documents. International Journal of Social Sciences and Management Research, 11(8), 534–557. https://doi.org/10.56201/ijssmr.vol.11no8.2025.pg534.557
Ominyi, M., Anichukwueze, C. C., & Uzougbo, N. S. (2025b). A review of intermediary liability and content governance obligations for digital platforms under the DSA, DMCA, and CDA regimes. International Journal of Social Sciences and Management Research, 11(8), 558–582. https://doi.org/10.56201/ijssmr.vol.11no8.2025.pg558.582
Ominyi, M., Anichukwueze, C. C., & Uzougbo, N. S. (2025c). Reviewing legal privilege and confidentiality challenges in enterprise deployment of large language models. Journal of Law and Global Policy, 10(3), 150–176. https://doi.org/10.56201/jlgp.vol.10.no3.2025.pg150.176
Ominyi, M., Anichukwueze, C. C., & Uzougbo, N. S. (2026). Conceptualizing enterprise AI governance functions as institutional standards for auditable and defensible risk management. Journal of Accounting and Financial Management, 12(7), 326–356. https://doi.org/10.56201/jafm.vol.12.no7.2026.p326.356
Orise, C., & Niniola, S. (2025). Data governance in digital health learning systems: A systematic review of implementation frameworks and best practices. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(5), 520–538.
Oshoba, T. O., Hammed, N. I., & Odejobi, O. D. (2020). Blockchain-enabled compliance and audit trail model for cloud configuration management. Journal of Frontiers in Multidisciplinary Research, 1(1), 193–201.
Oteri, O., & Edivri, J. (2026). An operational reliability and service assurance framework for enterprise IT systems supporting large user populations. Gulf Journal of Engineering & Technology, 2(1), 1–19. https://doi.org/10.51594/gjet.v2i1.205
Oyeleye, A. O., Dogbatsey, E. A., & Ebhojie, O. (2025). Embedding automated close controls in public sector ERP systems: A conceptual model for audit readiness and financial reporting quality. Zenodo. https://doi.org/10.5281/zenodo.20097950
Oyesiji, S. O., Nwakamma, S., & Ojukwu, J. (2023). Parameter-efficient multilingual adaptation of on-device language models: A survey. International Journal of Engineering and Modern Technology, 9(3), 287–320. https://doi.org/10.56201/ijcsmt.v9.no3.2023.pg287.320
Oyesiji, S. O., Nwakamma, S., & Ojukwu, J. (2024). A conceptual framework for context-aware data curation in fine-tuning pipelines. International Journal of Engineering and Modern Technology, 10(11), 197–228. https://doi.org/10.56201/ijemt.v10.no11.2024.pg197.228
Oyesiji, S. O., Nwakamma, S., & Ojukwu, J. (2025). Compressing recommender and ranking models for edge deployment: A survey. International Journal of Engineering and Modern Technology, 11(12), 205–237. https://doi.org/10.56201/ijemt.vol.11.no12.2025.pg205.237
Ozowara, D. E., Adebayo, A., & Anunagba, C. O. (2025). Advanced conceptual model for strengthening audit quality using data analytics across financial institutions. International Journal of Advanced Multidisciplinary Research and Studies, 5(6), 2246–2268. https://doi.org/10.62225/2583049X.2025.5.6.6050
Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77. https://doi.org/10.2753/MIS0742-1222240302
Peng, B., Galley, M., He, P., Cheng, H., Xie, Y., Hu, Y., Huang, Q., Liden, L., Yu, Z., Chen, W., & Gao, J. (2023). Check your facts and try again: Improving large language models with external knowledge and automated feedback. arXiv. https://doi.org/10.48550/arXiv.2302.12813
Petroni, F., Piktus, A., Fan, A., Lewis, P., Yazdani, M., De Cao, N., Thorne, J., Jernite, Y., Karpukhin, V., Maillard, J., Plachouras, V., Rocktäschel, T., & Riedel, S. (2021). KILT: A benchmark for knowledge intensive language tasks. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics, 2523–2544. https://doi.org/10.18653/v1/2021.naacl-main.200
Popay, J., Roberts, H., Sowden, A., Petticrew, M., Arai, L., Rodgers, M., Britten, N., Roen, K., & Duffy, S. (2006). Guidance on the conduct of narrative synthesis in systematic reviews: A product from the ESRC Methods Programme. Lancaster University.
Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N. A., & Lewis, M. (2023). Measuring and narrowing the compositionality gap in language models. Findings of the Association for Computational Linguistics: EMNLP 2023, 5687–5711. https://doi.org/10.18653/v1/2023.findings-emnlp.378
Ram, O., Levine, Y., Dalmedigos, I., Muhlgay, D., Shashua, A., Leyton-Brown, K., & Shoham, Y. (2023). In-context retrieval-augmented language models. Transactions of the Association for Computational Linguistics, 11, 1316–1331. https://doi.org/10.1162/tacl_a_00605
Rashkin, H., Nikolaev, V., Lamm, M., Aroyo, L., Collins, M., Das, D., Petrov, S., Tomar, G. S., Turc, I., & Reitter, D. (2023). Measuring attribution in natural language generation models. Computational Linguistics, 49(4), 777–840. https://doi.org/10.1162/coli_a_00486
Robertson, S., & Zaragoza, H. (2009). The probabilistic relevance framework: BM25 and beyond. Foundations and Trends in Information Retrieval, 3(4), 333–389. https://doi.org/10.1561/1500000019
Saad-Falcon, J., Khattab, O., Potts, C., & Zaharia, M. (2024). ARES: An automated evaluation framework for retrieval-augmented generation systems. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics, 338–354. https://doi.org/10.18653/v1/2024.naacl-long.20
Shah, R., Oziri, S. T., & Seyi-Lande, O. B. (2025). A framework for leveraging artificial intelligence in strategic business decision-making. Gulf Journal of Advance Business Research, 3(11), 1517–1558. https://doi.org/10.51594/gjabr.v3i11.171
Shi, F., Chen, X., Misra, K., Scales, N., Dohan, D., Chi, E. H., Schärli, N., & Zhou, D. (2023). Large language models can be easily distracted by irrelevant context. Proceedings of the International Conference on Machine Learning, 31210–31227.
Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Zettlemoyer, L., & Yih, W.-t. (2023). REPLUG: Retrieval-augmented black-box language models. arXiv. https://doi.org/10.48550/arXiv.2301.12652
Shuster, K., Poff, S., Chen, M., Kiela, D., & Weston, J. (2021). Retrieval augmentation reduces hallucination in conversation. Findings of the Association for Computational Linguistics: EMNLP 2021, 3784–3803. https://doi.org/10.18653/v1/2021.findings-emnlp.320
Singh, A., Ehtesham, A., Kumar, S., & Talaei Khoei, T. (2025). Agentic retrieval-augmented generation: A survey on agentic RAG. arXiv. https://doi.org/10.48550/arXiv.2501.09136
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., Payne, P., Seneviratne, M., Gamble, P., Kelly, C., Babiker, A., Schärli, N., Chowdhery, A., Mansfield, P., Demner-Fushman, D., … Natarajan, V. (2023). Large language models encode clinical knowledge. Nature, 620(7972), 172–180. https://doi.org/10.1038/s41586-023-06291-2
Thakur, N., Reimers, N., Rücklé, A., Srivastava, A., & Gurevych, I. (2021). BEIR: A heterogeneous benchmark for zero-shot evaluation of information retrieval models. Proceedings of the NeurIPS Datasets and Benchmarks Track.
Thorne, J., Vlachos, A., Christodoulopoulos, C., & Mittal, A. (2018). FEVER: A large-scale dataset for fact extraction and verification. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics, 809–819. https://doi.org/10.18653/v1/N18-1074
Tonoyan, A., Dada, O., & Ayivi-Donkor, S. S. (2024). Real-time KPI tracking systems: A review of automated performance monitoring and data-driven decision making. World Journal of Innovation and Modern Technology, 8(6), 247–281.
Trivedi, H., Balasubramanian, N., Khot, T., & Sabharwal, A. (2023). Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 10014–10037. https://doi.org/10.18653/v1/2023.acl-long.557
Venable, J., Pries-Heje, J., & Baskerville, R. (2016). FEDS: A framework for evaluation in design science research. European Journal of Information Systems, 25(1), 77–89. https://doi.org/10.1057/ejis.2014.36
Wohlin, C. (2014). Guidelines for snowballing in systematic literature studies and a replication in software engineering. Proceedings of the International Conference on Evaluation and Assessment in Software Engineering, Article 38. https://doi.org/10.1145/2601248.2601268
Xie, J., Zhang, K., Chen, J., Lou, R., & Su, Y. (2024). Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts. Proceedings of the International Conference on Learning Representations.
Yan, S.-Q., Gu, J.-C., Zhu, Y., & Ling, Z.-H. (2024). Corrective retrieval augmented generation. arXiv. https://doi.org/10.48550/arXiv.2401.15884
Yoran, O., Wolfson, T., Ram, O., & Berant, J. (2024). Making retrieval-augmented language models robust to irrelevant context. Proceedings of the International Conference on Learning Representations.
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