Skip to main content
Have a personal or library account? Click to login
GenAI in the Military: Trends and Opportunities Cover

GenAI in the Military: Trends and Opportunities

By:  and    
Open Access
|Nov 2025

References

  1. Ali, M., Fromm, M., Thellmann, K., Ebert, J., Weber, A. A., Rutmann, R., Jain, C., Lübbering, M., Steinigen, D., Leveling, J., Klug, K., Schulze Buschhoff, J., Jurkschat, L., Abdelwahab, H., Stein, B. J., Sylla, K.-H., Denisov, P., Brandizzi, N., Saleem, Q., Bhowmick, A., Helmer, L., John, C., Ortiz Suarez, P., Ostendorff, M., Jude, A., Manjunath, L., Weinbach, S., Penke, C., Filatov, O., Asaadi, S., Barth, F., Sifa, R., Küch, F., Herten, A., Jäkel, R., Rehm, G., Kesselheim, S., Köhler, J., & Flores-Herr, N. (2024). Teuken-7B-Base & Teuken-7B-Instruct: towards European LLMs. arXiv. 10.3233/FAIA251328
  2. Andreoni, M., Lunardi, W. T., Lawton, G., & Thakkar, S. (2024). Enhancing autonomous system security and resilience with Generative AI: A comprehensive survey. IEEE Access, 12, 109470109493. 10.1109/ACCESS.2024.3439363
  3. Ateia, S., & Kruschwitz, U. (2024). Can open-source LLMs compete with commercial models? Exploring the few-shot performance of current GPT models in biomedical tasks. arXiv. https://arxiv.org/abs/2407.13511
  4. Ba, L. J., & Caruana, R. (2014). Do deep nets really need to be deep? arXiv. https://arxiv.org/abs/1312.6184
  5. Balis, C., & O’Neill, P. (2022). Trust in AI: Rethinking future command (Occasional paper). Royal United Services Institute for Defence and Security Studies.
  6. Barzyk, C., Hickson, J., Ochoa, J., Talley, J., Willeke, M., Coffey, S., Pavlik, J., & Bastian, N. D. (2024). A generative artificial intelligence methodology for automated zero-shot data tagging to support tactical zero trust architecture implementation. Proceedings of the Annual General Donald R. Keith Memorial Conference. 10.37266/ISER.2025v12i2.pp83-88
  7. Beauchamp-Mustafaga, N., Green, K., Marcellino, W., Lilly, S., & Smith, J. (2024). Dr. Li Bicheng, or how China learned to stop worrying and love social media manipulation (Technical report). RAND Corporation.
  8. Behrouz, A., Zhong, P., & Mirrokni, V. (2024). Titans: Learning to memorize at test time. arXiv. https://arxiv.org/abs/2501.00663
  9. Bishop, C. M., & Bishop, H. (2023). Deep learning: foundations and concepts. Springer Nature. 10.1007/978-3-031-45468-4
  10. Black, J., Eken, M., Parakilas, J., Dee, S., Ellis, C., Suman-Chauhan, K., Bain, R., Fine, H., Aquilino, M. C., Lebret, M., & Palicka, O. (2024). Strategic competition in the age of AI: Emerging risks and opportunities from military use of artificial intelligence. RAND.
  11. Blanchard, A., & Bruun, L. (2024). Bias in military artificial intelligence. SIPRI Publications. 10.55163/CJFT9557
  12. Bode, I., & Bhila, I. (2024, September 3). The problem of algorithmic bias in AI-based military decision support systems. Humanitarian Law and Policy.
  13. Boone, T., Fahimnia, B., Ganeshan, R., Herold, D. M., & Sanders, N. R. (2025). Generative AI: Opportunities, challenges, and research directions for supply chain resilience. Transportation Research Part E: Logistics and Transportation Review, 199, 104135. 10.1016/j.tre.2025.104135
  14. Brearcliffe, D. K. (2023). Computational military science: Complexity, broken symmetry, and artificial intelligence [Unpublished doctoral dissertation]. George Mason University.
  15. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Girish, S., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., … Amodei, D. (2020). Language models are few-shot learners. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems (Vol. 33, pp. 18771901). Curran Associates, Inc. https://proceedings.neurips.cc/paperfiles/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
  16. Caballero, W. N., & Jenkins, P. R. (2025). On large language models in national security applications. Stat, 14(2), e70057. 10.1002/sta4.70057
  17. Cardillo, A. (2025). Best 39 large language models (LLMs) in 2025. Exploding Topics. https://explodingtopics.com/blog/list-of-llms
  18. Chuang, H. M., & Cheng, D. W. (2022). Conversational AI over military scenarios using intent detection and response generation. Applied Sciences, 12(5), 2494. 10.3390/app12052494
  19. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 41714186). Association for Computational Linguistics.
  20. Farabet, C., & Warkentin, W. (2025, May 12). Introducing Gemma 3: The most capable model you can run on a single GPU or TPU. Google. https://blog.google/technology/developers/gemma-3/
  21. Feffer, M., Sinha, A., Deng, W. H., Lipton, Z. C., & Heidari, H. (2024). Red-teaming for generative AI: Silver bullet or security theater? In Proceedings of the 2024 AAAI/ACM Conference on AI, Ethics, and Society (Vol. 7, pp. 421437). ACM. 10.1609/aies.v7i1.31647
  22. Garcia, D. (2024). Algorithms and decision-making in military artificial intelligence. Global Society, 38(1), 2433. 10.1080/13600826.2023.2273484
  23. Geist, E., Frank, A. B., & Menthe, L. (2024). Limits of artificial intelligence for warfighters (Technical report). RAND Corporation.
  24. Goecks, V. G., & Waytowich, N. (2024). COA-GPT: Generative pre-trained transformers for accelerated course of action development in military operations. In 2024 International Conference on Military Communication and Information Systems (ICMCIS) (pp. 110). IEEE. 10.1109/ICMCIS61231.2024.10540749
  25. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems (Vol. 27, pp. 26722680). Curran Associates, Inc.
  26. Grand-Clément, S. (2023). Artificial intelligence beyond weapons: Application and impact of AI in the military domain. UNIDIR.
  27. Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., Bi, X., Zhang, X., Yu, X., Wu, Y., Wu, Z. F., Gou, Z., Shao, Z., Li, Z., Gao, Z., Liu, A., Xue, B., Wang, B., Wu, B., Feng, B., Lu, C., Zhao, C., Deng, C., Zhang, C., Ruan, C., Dai, D., Chen, D., Ji, D., Li, E., Lin, F., Dai, F., Luo, F., Hao, G., Chen, G., Li, G., Zhang, H., Bao, H., Xu, H., Wang, H., Ding, H., Xin, H., Gao, H., Qu, H., Li, H., Guo, J., Li, J., Wang, J., Chen, J., Yuan, J., Qiu, J., Li, J., Cai, J. L., Ni, J., Liang, J., Chen, J., Dong, K., Hu, K., Gao, K., Guan, K., Huang, K., Yu, K., Wang, L., Zhang, L., Zhao, L., Wang, L., Zhang, L., Xu, L., Xia, L., Zhang, M., Zhang, M., Tang, M., Li, M., Wang, M., Li, M., Tian, N., Huang, P., Zhang, P., Wang, Q., Chen, Q., Du, Q., Ge, R., Zhang, R., Pan, R., Wang, R., Chen, R. J., Jin, R. L., Chen, R., Lu, S., Zhou, S., Chen, S., Ye, S., Wang, S., Yu, S., Zhou, S., Pan, S., Li, S. S., Zhou, S., Wu, S., Ye, S., Yun, T., Pei, T., Sun, T., Wang, T., Zeng, W., Zhao, W., Liu, W., Liang, W., Gao, W., Yu, W., Zhang, W., Xiao, W. L., An, W., Liu, X., Wang, X., Chen, X., Nie, X., Cheng, X., Liu, X., Xie, X., Liu, X., Yang, X., Li, X., Su, X., Lin, X., Li, X. Q., Jin, X., Shen, X., Chen, X., Sun, X., Wang, X., Song, X., Zhou, X., Wang, X., Shan, X., Li, Y. K., Wang, Y. Q., Wei, Y. X., Zhang, Y., Xu, Y., Li, Y., Zhao, Y., Sun, Y., Wang, Y., Yu, Y., Zhang, Y., Shi, Y., Xiong, Y., He, Y., Piao, Y., Wang, Y., Tan, Y., Ma, Y., Liu, Y., Guo, Y., Ou, Y., Wang, Y., Gong, Y., Zou, Y., He, Y., Xiong, Y., Luo, Y., You, Y., Liu, Y., Zhou, Y., Zhu, Y. X., Xu, Y., Huang, Y., Li, Y., Zheng, Y., Zhu, Y., Ma, Y., Tang, Y., Zha, Y., Yan, Y., Ren, Z. Z., Ren, Z., Sha, Z., Fu, Z., Xu, Z., Xie, Z., Zhang, Z., Hao, Z., Ma, Z., Yan, Z., Wu, Z., Gu, Z., Zhu, Z., Liu, Z., Li, Z., Xie, Z., Song, Z., Pan, Z., Huang, Z., Xu, Z., Zhang, Z., & Zhang, Z. (2025). DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning. arXiv. 10.1038/s41586-025-09422-z
  28. Gupta, M., & Agrawal, P. (2021). Compression of deep learning models for text: A survey. arXiv. https://arxiv.org/abs/2008.05221
  29. Hinton, P. (2023). Generative AI and wargaming: What is it good for? The RUSI Journal, 168(7), 3441. 10.1080/03071847.2023.2282863
  30. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 17351780. 10.1162/neco.1997.9.8.1735
  31. Hua, W., Fan, L., Li, L., Mei, K., Ji, J., Ge, Y., Hemphill, L., & Zhang, Y. (2024). War and peace (WarAgent): Large language model-based multi-agent simulation of world wars. arXiv. https://arxiv.org/abs/2311.17227
  32. Huang, Z., Zhang, X., Tang, Z., Xu, F., Datcu, M., & Han, J. (2025). Generative artificial intelligence meets synthetic aperture radar: A survey. IEEE Geoscience and Remote Sensing Magazine. 10.1109/MGRS.2024.3483459
  33. Iacob, A., Sani, L., Marino, B., Aleksandrov, P., Shen, W. F., & Lane, N. D. (2024). Worldwide federated training of language models. Proceedings of the FL@FM-NeurIPS.
  34. Ji, S., Tan, Y., Saravirta, T., Yang, Z., Vasankari, L., Pan, S., Guodong, L., & Walid, A. (2024). Emerging trends in federated learning: From model fusion to federated X learning. International Journal of Machine Learning and Cybernetics, 15, 37693790. 10.1007/s13042-024-02119-1
  35. 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), 138. 10.1145/3571730
  36. Kelly, P., & Smith, H. (2024). How to think about integrating generative AI in professional military education. Military Review.
  37. Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X. H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. arXiv. https://arxiv.org/abs/2506.08872
  38. Lee, C. E., Baek, J., Son, J., & Ha, Y. G. (2023). Deep AI military staff: Cooperative battlefield situation awareness for commander’s decision making. The Journal of Supercomputing, 79(6), 60406069. 10.1007/s11227-022-04882-w
  39. Lee, Y., Park, T., Lee, Y., Gong, J., & Kang, J. (2025). Exploring potential prompt injection attacks in federated military LLMs and their mitigation. arXiv. https://arxiv.org/abs/2501.18416
  40. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M. N., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems (Vol. 33, pp. 94599474). Curran Associates, Inc. https://proceedings.neurips.cc/paperfiles/paper/2020/file/6b493230205f780e1bc26945df7481e5-Paper.pdf
  41. Li, X., Feng, J., Meng, Y., Han, Q., Wu, F., & Li, J. (2019). A unified MRC framework for named entity recognition. arXiv. 10.18653/v1/2020.acl-main.519
  42. Lin, S., Hua, W., Li, L., Chang, C.-J., Fan, L., Ji, J., Hua, H., Jin, M., Luo, J., & Zhang, Y. (2024). BattleAgent: Multi-modal dynamic emulation on historical battles to complement historical analysis. arXiv. 10.18653/v1/2024.emnlp-demo.18
  43. Liu, G., Van Huynh, N., Du, H., Hoang, D. T., Niyato, D., Zhu, K., Kang, J., Xiong, Z., Jamalipour, A., & Kim, D. I. (2 L. (2024). Generative AI for unmanned vehicle swarms: Challenges, applications and opportunities. arXiv. https://arxiv.org/abs/2402.18062
  44. Liu, T., Liu, J., Zhang, T., Wang, J., Wang, J., Kang, J., Niyato, D., & Mao, S. (2025). Generative AI-driven cross-layer covert communication: Fundamentals, framework and case study. arXiv. 10.1109/MCOM.004.2500025
  45. Liu, X., Yu, Z., Liu, X., Miao, L., & Yang, T. (2024). Military equipment entity extraction based on large language model. Applied Sciences, 14(19), 9063. 10.3390/app14199063
  46. LLM Stats. (2025). LLM leaderboard 2025 – Verified AI rankings. Retrieved July 2, 2025, from https://llm-stats.com/
  47. Luukkonen, R., Burdge, J., Zosa, E., Talman, A., Komulainen, V., Hatanpää, V., Sarlin, P., & Pyysalo, S. (2024). Poro 34b and the blessing of multilinguality. arXiv. https://arxiv.org/abs/2404.01856
  48. Marcellino, W., Beauchamp-Mustafaga, N., Kerrigan, A., Chao, L. N., & Smith, J. (2023). The rise of generative AI and the coming era of social media manipulation 3.0: Next-generation Chinese astroturfing and coping with ubiquitous AI (Technical report). RAND Corporation.
  49. Martins, P. H., Alves, J., Fernandes, P., Guerreiro, N. M., Rei, R., Farajian, A., Klimaszewski, M., Alves, D. M., Pombal, J., Boizard, N., Faysse, M., Colombo, P., Yvon, F., Haddow, B., de Souza, J. G. C., Birch, A., & Martins, A. F. T. (2025). EuroLLM-9B: Technical report. arXiv. https://arxiv.org/abs/2506.04079
  50. Martins, P. H., Fernandes, P., Alves, J., Guerreiro, N. M., Rei, R., Alves, D. M., Pombal, J. P., Farajian, A., Faysse, M., Klimaszewski, M., Colombo, P., Haddow, B., de Souza, J. C., Birch, A., & Martins, A. (2024). EuroLLM: Multilingual language models for Europe. arXiv. 10.1016/j.procs.2025.02.260
  51. Maslej, N., et al. (2024). Chapter 4: Economy. In Artificial Intelligence Index Report 2024 (pp. 4668). Stanford Institute for Human-Centered Artificial Intelligence.
  52. Matarazzo, A., & Torlone, R. (2025). A survey on large language models with some insights on their capabilities and limitations. arXiv. https://arxiv.org/abs/2501.04040
  53. McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. arXiv. https://arxiv.org/abs/1602.05629
  54. Menthe, L., Zhang, L., Geist, E., Steier, J., Frank, A. B., van Hegewald, E., Briggs, G. J., Scholl, K., Ashpari, Y., & Jacques, A. (2024). Limits of artificial intelligence for warfighters (Technical report). RAND Corporation.
  55. Mikhailov, D. (2023). Optimizing national security strategies through LLM-driven artificial intelligence integration. arXiv. 10.14293/PR2199.000136.v1
  56. Moy, W. R., & Gradon, K. T. (2023). Artificial intelligence in hybrid and information warfare. In M. Cristiano, et al. (Eds.), AI and international conflict in cyberspace (pp. 4874). RAND Corporation. 10.4324/9781003284093-4
  57. Nadibaidze, A., Bode, I., & Zhang, Q. (2024). AI in military decision support systems: A review of developments and debates (Review). Center for War Studies, University of Southern Denmark.
  58. Narajala, V. S., & Narayan, O. (2025). Securing Agentic AI: A comprehensive threat model and mitigation framework for Generative AI agents. arXiv. https://arxiv.org/abs/2504.19956
  59. Narayanan, P., Vindiola, M., Park, S., Logie, A., Waytowich, N., Mittrick, M., Richardson, J., Asher, D., & Kott, A. (2021). First-year report of ARL directors strategic initiative (fy20–23): Artificial intelligence (AI) for command and control (C2) of multi-domain operations (MDO) (ARL-TR-9192). DEVCOM Army Research Laboratory.
  60. Oniani, D., Hilsman, J., Peng, Y., Poropatich, R. K., Pamplin, J. C., Legault, G. L., & Wang, Y. (2023). Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare. NPJ Digital Medicine, 6(1), 225. 10.1038/s41746-023-00965-x
  61. Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann. 10.1016/C2009-0-27609-4
  62. Perez, F., & Ribeiro, I. (2022). Ignore previous prompt: Attack techniques for language models. arXiv. https://arxiv.org/abs/2211.09527
  63. Radford, A., Metz, L., & Chintala, S. (2016). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv. https://arxiv.org/abs/1511.06434
  64. Rashid, A. B., Kausik, A. K., Al Hassan Sunny, A., & Bappy, M. H. (2023). Artificial intelligence in the military: An overview of the capabilities, applications, and challenges. International Journal of Intelligent Systems, 2023(1), 8676366. 10.1155/2023/8676366
  65. Rettore, P. H., Zißner, P., Alkhowaiter, M., Zou, C., & Sevenich, P. (2024). Military data space: Challenges, opportunities, and use cases. IEEE Communications Magazine (pp. 7076). 10.1109/MCOM.001.2300396
  66. Rivera, J. P., Mukobi, G., Reuel, A., Lamparth, M., Smith, C., & Schneider, J. (2024). Escalation risks from language models in military and diplomatic decision-making. In The 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 836898). ACM. 10.1145/3630106.3658942
  67. Ruiz, D. C., & Sell, J. (2024). Fine-tuning and evaluating open-source large language models for the Army domain. arXiv. 10.48550/arXiv.2410.20297
  68. Sani, L., Iacob, A., Cao, Z., Marino, B., Gao, Y., Paulik, T., Zhao, W., Shen, W. F., Aleksandrov, P., Qiu, X., & Lane, N. D. (2024). The future of large language model pre-training is federated. Proceedings of the FL@FM-NeurIPS.
  69. Schneider, J. (2024). Explainable generative AI (GenXAI): A survey, conceptualization, and research agenda. Artificial Intelligence Review, 57(11), 289. 10.1007/s10462-024-10916-x
  70. Schneider, J. (2025). Mental model shifts in human-LLM interactions. Journal of Intelligent Information Systems. 10.1007/s10844-025-00960-6
  71. Shavit, Y., Agarwal, S., Brundage, M., Adler, S., O’Keefe, C., Campbell, R., Lee, T., Mishkin, P., Eloundou, T., Hickey, A., Slama, K., Ahmad, L., McMillan, P., Vallone, A., Passos, A., & Robinson, D. G. (2023). Practices for governing agentic AI systems. OpenAI. https://openai.com/index/practices-for-governing-agentic-ai-systems/
  72. Shayea, G., Zabil, M., Habeeb, M., Khaleel, Y., & Albahri, A. (2025). Strategies for protection against adversarial attacks in AI models: An in-depth review. Journal of Intelligent Systems, 34(1), 20240277. 10.1515/jisys-2024-0277
  73. Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., & Dean, J. (2017). Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv. https://arxiv.org/abs/1701.06538
  74. Simon, H. A. (1977). The new science of management decision (Rev. ed.). Prentice Hall.
  75. Sprague, R. H. (1980). A framework for the development of decision support systems. MIS Quarterly, 4(4), 126. 10.2307/248875
  76. Stebbins, D., Girven, R. S., Parker, T., Deen, T., De Bruhl, B., Ryseff, J., Paige, J. W., Kleiman, A. Y., Bhatt, S. D., Sousa, É. M., Kepe, M., & Fay, M. (2024). Exploring artificial intelligence use to mitigate potential human bias within US Army Intelligence Preparation of the Battlefield processes (Report No. RRA2763-1). RAND Corporation.
  77. Tian, Y., Lin, F., Li, Y., Zhang, T., Zhang, Q., Fu, X., Huang, J., Dai, X., Wang, Y., Tian, C., Li, B., Lv, Y., Kovács, L., & Wang, F. Y. (2025). UAVs meet LLMs: Overviews and perspectives toward agentic low-altitude mobility. Information Fusion, 122, 103158. 10.1016/j.inffus.2025.103158
  78. Uddin, M., Irshad, M. S., Kandhro, I. A., Alanazi, F., Ahmed, F., Maaz, M., Hussain, S., & Ullah, S. S. (2025). Generative AI revolution in cybersecurity: A comprehensive review of threat intelligence and operations. Artificial Intelligence Review, 58(8), 139. 10.1007/s10462-025-11219-5
  79. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. In I. Guyon, U. von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (Vol. 30). Curran Associates, Inc.
  80. Vinyals, O., Ewalds, T., Bartunov, S., Georgiev, P., Vezhnevets, A. S., Yeo, M., Makhzani, A., Küttler, H., Agapiou, J., & Schrittwieser, J. (2017). StarCraft II: A new challenge for reinforcement learning. arXiv. https://arxiv.org/abs/1708.04782
  81. Wang, S., Sun, X., Li, X., Ouyang, R., Wu, F., Zhang, T., Li, J., & Wang, G. (2023). GPT-NER: Named entity recognition via large language models. arXiv. https://arxiv.org/abs/2304.10428
  82. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. In Proceedings of the 36th International Conference on Neural Information Processing Systems (NIPS ’22). Curran Associates Inc.
  83. Weizenbaum, J. (1966). ELIZA—A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 3645. 10.1145/365153.365168
  84. Xu, X., Li, M., Tao, C., Shen, T., Cheng, R., Li, J., Xu, C., Tao, D., & Zhou, T. (2024). A survey on knowledge distillation of large language models. arXiv. https://arxiv.org/abs/2402.13116
  85. Yigit, Y., Buchanan, W. J., Tehrani, M. G., & Maglaras, L. (2024). Review of Generative AI methods in cybersecurity. arXiv. https://arxiv.org/abs/2403.08701
  86. Zarrar, H., & Kakar, S. A. (2024). Generative artificial intelligence & its military applications by the US and China – lessons for south Asia. Journal of Computing & Biomedical Informatics.
  87. Zhang, P., Zeng, G., Wang, T., & Lu, W. (2024). TinyLlama: An open-source small language model. arXiv. https://arxiv.org/abs/2401.02385
DOI: https://doi.org/10.31374/sjms.415 | Journal eISSN: 2596-3856
Language: English
Page range: 416 - 434
Submitted on: Apr 4, 2025
Accepted on: Oct 30, 2025
Published on: Nov 24, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2025 Lauri Vasankari, Aapo Koski, published by Scandinavian Military Studies
This work is licensed under the Creative Commons Attribution 4.0 License.