Mukherjee, Krishnendu (2025): An Adaptive RAG-Based Question-Answering System in the Context of Industry 5.0. Published in: Journal of Computational Analysis and Applications (JoCAAA) , Vol. 34, No. 7 (30 July 2025): pp. 259-274.
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Abstract
In this paper, a pragmatic literature review approach has been shown to select research papers to determine important technologies in the context of Industry 5.0 or I 5.0 such as Artificial Intelligence (AI), Internet of Things (IOT), Collaborative Robot (Cobot), Cyber Physical System (CPS), Human Machine Interface (HMI), Edge Computing, Big Data, Digital Twin, Virtual Reality (VR), Reinforcement Learning (RL), Large Language Model (LLM), Multiple Criteria Decision Analysis (MCDA) etc. The crux of this paper is to develop an economical Adaptive RAG-based (ARAG) system which could generate contextual relevant responses to a user’s query. A two-stage hybrid zero-resource hallucination detection system has been developed to detect hallucinations in the generated response. A binary classifier has been developed using Mistral 7B for fact checking against reliable resources and a panel of multiple Large Language Models (LLMs), namely, Mistral 7B, Llama 3 8B, and Llama 2 7B, has been used to evaluate the factual accuracy of the generated response asynchronously using the 5-point Agreement Scale. Mistral 7B has shown a very high correlation with human judges. Open source or free resources are used to develop the ARAG, and, thus, the ARAG is economical. A brief discussion on multilingual responses is also included.
Item Type: | MPRA Paper |
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Original Title: | An Adaptive RAG-Based Question-Answering System in the Context of Industry 5.0 |
Language: | English |
Keywords: | Large language model; Adaptive rag; Industry 5.0; Rag; Mistral; Hallucination |
Subjects: | C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C88 - Other Computer Software |
Item ID: | 125534 |
Depositing User: | Dr Krishnendu Mukherjee |
Date Deposited: | 02 Aug 2025 14:28 |
Last Modified: | 02 Aug 2025 14:28 |
References: | 1. Ahmed I, Hossain NUI, Fazio SA, Lezzi M, Islam MdS. A decision support model for assessing and prioritization of industry 5.0 cybersecurity challenges. Sustainable Manufacturing and Service Economics [Internet]. 2024 Jan 1;3:100018. Available from: https://www.sciencedirect.com/science/article/pii/S266734442400001X 2. Abdullah Alabdulatif, Navod Neranjan Thilakarathne, Zaharaddeen Karami Lawal. A Review on Security and Privacy Issues Pertaining to Cyber-Physical Systems in the Industry 5.0 Era. Computers, materials & continua/Computers, materials & continua (Print) [Internet]. 2024 Jan 1;80(3):3917–43. Available from: https://www.sciencedirect.com/org/science/article/pii/S1546221824006507 3. Alonso R, Haber RE, Castaño F, Diego Reforgiato Recupero. Interoperable Software Platforms for Introducing Artificial Intelligence Components in Manufacturing: A Meta-Framework for Security and Privacy. Heliyon. 2024 Feb 1;10(4):e26446–6. 4. Ansari F, Erol S, Sihn W. Rethinking Human-Machine Learning in Industry 4.0: How Does the Paradigm Shift Treat the Role of Human Learning? Procedia Manufacturing. 2018;23:117–22. 5. Barata J, Kayser I. How will the digital twin shape the future of industry 5.0? Technovation. 2024 Jun 1;134:103025–5. 6. Breque M, De Nul L, Petridis A. Industry 5.0 Towards a sustainable, Humancentric and Resilient European Industry [Internet]. European Commission; 2021 [cited 2025 Jun 3]. Available from: https://eurocid.mne.gov.pt/sites/default/files/repository/paragraph/documents/17991/brochura-industry-50_0.pdf 7. Chang Y, Wang X, Wang J, Yuan W, Yang L, Zhu K, et al. A Survey on Evaluation of Large Language Models. ACM Transactions on Intelligent Systems and Technology. 2024 Jan 23;15(3):1–45. 8. Chand S, Lu Y. Dual task scheduling strategy for personalized multi-objective optimization of cycle time and fatigue in human-robot collaboration. Manufacturing Letters. 2023 Aug;35:88–95. 9. Garrido S, Muniz J, Batista Ribeiro V. Operations Management, Sustainability & Industry 5.0: A critical analysis and future agenda. Cleaner Logistics and Supply Chain [Internet]. 2024 Mar 1;10:100141. Available from: https://www.sciencedirect.com/science/article/pii/S2772390924000039 10. Gao M, Ruan J, Sun R, Yin X, Yang S, Wan X. Human-like Summarization Evaluation with ChatGPT [Internet]. 2023. Available from: https://arxiv.org/pdf/2304.02554 11. Morteza Ghobakhloo, Iranmanesh M, Tseng ML, Andrius Grybauskas, Stefanini A, Azlan Amran. Behind the definition of Industry 5.0: a systematic review of technologies, principles, components, and values. Journal of Industrial and Production Engineering. 2023 May 27;40(6):1–16. 12. Ramtin Haghnazar, Yasaman Ashjazadeh, Hauptman J, Nasir V. A Computational Design Integrated Digital Fabrication Framework for Mass Customization in Industry 5.0 Manufacturing with Non-Standard Natural Materials. Results in Engineering. 2024 Jun 11;23:102400–0. 13. Jeong S, Baek J, Cho S, Hwang SJ, Park J. Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity. arXiv (Cornell University) [Internet]. 2024 Jan 1 [cited 2025 Jun 3]; Available from: https://aclanthology.org/2024.naacl-long.389/ 14. Kasneci E, Sessler K, Küchemann S, Bannert M, Dementieva D, Fischer F, et al. ChatGPT for good? on Opportunities and Challenges of Large Language Models for Education. Learning and Individual Differences [Internet]. 2023 Apr 1;103. Available from: https://www.sciencedirect.com/science/article/abs/pii/S1041608023000195 15. Kahiomba Sonia Kiangala, Wang Z. An experimental hybrid customized AI and generative AI chatbot human machine interface to improve a factory troubleshooting downtime in the context of Industry 5.0. The International Journal of Advanced Manufacturing Technology. 2024 Apr 3;132. 16. Nauman A, Khan WU, Ghadah Aldehim, Alqahtani H, Nuha Alruwais, Mesfer Al Duhayyim, et al. Communication and computational resource optimization for Industry 5.0 smart devices empowered by MEC. Journal of King Saud University - Computer and Information Sciences. 2023 Dec 15;36(1):101870–0. 17. Rožanec JM, Novalija I, Zajec P, Kenda K, Tavakoli Ghinani H, Suh S, et al. Human-centric artificial intelligence architecture for industry 5.0 applications. International Journal of Production Research. 2022 Nov 7;61(20):1–26. 18. Russell S, Norvig P. Artificial Intelligence: A Modern Approach. Prentice Hall, New Jersey, ISBN 0-13-103805-2.; 2003. 19. Sai S, Sai R, Vinay Chamola. Generative AI for Industry 5.0: Analyzing the impact of ChatGPT, DALLE, and Other Models. IEEE open journal of the Communications Society. 2024 Jan 1;1–1. 20. Shahbakhsh M, Emad GR, Cahoon S. Industrial revolutions and transition of the maritime industry: The case of Seafarer’s role in autonomous shipping. The Asian Journal of Shipping and Logistics. 2022 Mar;38(1):10–8. 21. Turner CJ, Garn W. Next generation DES simulation: A research agenda for human centric manufacturing systems. Journal of Industrial Information Integration. 2022 Jul;28:100354. 22. Verma A, Bhattacharya P, Madhani N, Trivedi C, Bhushan B, Tanwar S, et al. Blockchain for Industry 5.0: Vision, Opportunities, Key Enablers, and Future Directions. IEEE Access. 2022;10:69160–99. 23. Ljubo Vlacic, Huang H, Mariagrazia Dotoli, Wang Y, Ioannou PA, Fan L, et al. Automation 5.0: The Key to Systems Intelligence and Industry 5.0. IEEE/CAA Journal of Automatica Sinica. 2024 Jul 19;11(8):1723–7. 24. Wang ZJ, Chen Z, Xiao L, Su Q, Govindan K, Skibniewski MJ. Blockchain adoption in sustainable supply chains for Industry 5.0: A multistakeholder perspective. Journal of Innovation & Knowledge. 2023 Oct 1;8(4):100425–5. 25. Wang FY, Yang J, Wang X, Li J, Han QL. Chat with ChatGPT on Industry 5.0: Learning and Decision-Making for Intelligent Industries. IEEE/CAA Journal of Automatica Sinica [Internet]. 2023 Apr 1;10(4):831–4. Available from: https://ieeexplore.ieee.org/abstract/document/10085975/ 26. Williamson SM, Prybutok V. Integrating human-centric automation and sustainability through the NAToRM framework: A neuromorphic computing approach for resilient industry 5.0 supply chains. International Journal of Information Management Data Insights. 2024 Nov 1;4(2):100278–8. 27. Xu Z, Jain S, Kankanhalli M. Hallucination is Inevitable: An Innate Limitation of Large Language Models [Internet]. 2024. Available from: https://arxiv.org/pdf/2401.11817 28. Xu X, Lu Y, Vogel-Heuser B, Wang L. Industry 4.0 and Industry 5.0—Inception, conception and perception. Journal of Manufacturing Systems [Internet]. 2021 Oct;61(1):530–5. Available from: https://www.sciencedirect.com/science/article/pii/S0278612521002119 29. Yang J, Liu Y, Morgan PL. Human–machine interaction towards Industry 5.0: Human-centric smart manufacturing. Digital Engineering. 2024 Aug 1;2:100013–3. 30. Yang T, Razzaq L, H. Fayaz, Qazi A. Redefining fan manufacturing: Unveiling industry 5.0’s human-centric evolution and digital twin revolution. Heliyon. 2024 Jul 1;10(13):e33551–1. 31. Zafar MH, Bukhari SMS, Abou Houran M, Moosavi SKR, Mansoor M, Al-Tawalbeh N, et al. Step towards secure and reliable smart grids in Industry 5.0: A federated learning assisted hybrid deep learning model for electricity theft detection using smart meters. Energy Reports [Internet]. 2023 Nov 1;10:3001–19. Available from: https://www.sciencedirect.com/science/article/pii/S2352484723013458 32. Zhao Y, Zhao J, Jiang L, Tan R, Niyato D, Li Z, et al. Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices. IEEE Internet of Things Journal. 2020;8(3):1817–29. 33. Steffen V, de Oliveira MS, Trojan F. A Novel Approach for Systematic Literature Reviews Using Multi-Criteria Decision Analysis. Journal of Intelligent Management Decision. 2024 May 23;3(2):116–38. 34. Qin L, Chen Q, Zhou Y, Chen Z, Li Y, Liao L, et al. A survey of multilingual large language models. Patterns. 2025 Jan;6(1):101118. 35. Moslem Y, Haque R, Way A. Fine-tuning Large Language Models for Adaptive Machine Translation. arXiv (Cornell University). 2023 Dec 19; 36. Ono K, Morita A. Evaluating Large Language Models: ChatGPT-4, Mistral 8x7B, and Google Gemini Benchmarked Against MMLU. 2024 Mar 4 [cited 2024 Nov 2]; Available from: https://www.techrxiv.org/users/748222/articles/719880-evaluating-large-language-models-chatgpt-4-mistral-8x7b-and-google-gemini-benchmarked-against-mmlu [37] Manakul P, Liusie A, Gales MJF. SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models. arXiv:230308896 [cs] [Internet]. 2023 Mar 15; Available from: https://arxiv.org/abs/2303.08896 [38] Azaria A, Mitchell T. The Internal State of an LLM Knows When its Lying [Internet]. arXiv.org. 2023. Available from: https://arxiv.org/abs/2304.13734 [39] Bai Y, Ying J, Cao Y, Xin Lv, He Y, Wang X, Yu J, Zeng K, Xiao Y, Lyu H, Zhang J, Li J, and Hou L. Benchmarking Foundation Models with Language-Model-as-an-Examiner. arXiv (Cornell University). 2023 Jun 7 [40] Verga P, Hofstatter S, Althammer S, Su Y, Piktus A, Arkhangorodsky A, Xu M, and Lewis N W P. Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models [Internet]. arXiv.org. 2024. Available from: https://arxiv.org/abs/2404.18796 [41] Bouchard D and Chauhan MS. Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorers [Internet]. arXiv.org. 2025 [cited 2025 Jul 4]. Available from: https://arxiv.org/abs/2504.19254 |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/125534 |