20.500.12556/RUP-21786 Is open source the future of AI? a data-driven approach Is open source the future of AI? A data-driven approach Large language models (LLMs) have become central to both academic research and industrial applications, fueling debates on their accuracy, usability, privacy, and potential misuse. While proprietary models benefit from substantial investments in data and computing resources, open-sourcing is often suggested as a means to enhance trust and transparency. Yet, open-sourcing comes with its own challenges, such as risks of illicit applications, limited financial incentives, and intellectual property concerns. Positioned between these extremes are hybrid approaches—including partially open models and licensing restrictions—that aim to balance openness with control. In this paper, we adopt a data-driven approach to examine the open-source development of LLMs. By analyzing contributions in model improvements, modifications, and methodologies, we assess how community efforts impact model performance. Our findings indicate that the open-source community can significantly enhance models, demonstrating that community-driven modifications can yield efficiency gains without compromising performance. Moreover, our analysis reveals distinct trends in community growth and highlights which architectures benefit disproportionately from open-source engagement. These insights provide an empirical foundation to inform balanced discussions among industry experts and policymakers on the future direction of AI development. large language models artificial intelligence open source data science HuggingFace veliki jezikovni modeli umetna inteligenca odprta koda podatkovna znanost HuggingFace true false false Angleški jezik Slovenski jezik Članek v reviji 2025-09-25 15:00:17 2025-09-25 15:00:18 2025-10-16 03:04:59 0000-00-00 00:00:00 2025 0 0 str. 1-18 iss. 5, [article no.] 2790 Vol. 15 Mar.-1 2025 0000-00-00 Zaloznikova NiDoloceno NiDoloceno 0000-00-00 0000-00-00 2025-03-05 004.8 2076-3417 10.3390/app15052790 228028675 RAZ_Vake_Domen_2025.pdf RAZ_Vake_Domen_2025.pdf 1 92A7906B18A94EF040FF44A7D6A66C78 d8b39fc5bc63a9aeb5212b871bfa101dca28449c25f8b9ec764af0ae9320aa96 fa1f451e-9a0f-11f0-8f0b-005056ac49c0 https://repozitorij.upr.si/Dokument.php?lang=slv&id=31673 https://www.mdpi.com/2076-3417/15/5/2790 1 b61127fd-9a0f-11f0-8f0b-005056ac49c0 https://repozitorij.upr.si/Dokument.php?lang=slv&id=31672 Fakulteta za matematiko, naravoslovje in informacijske tehnologije 0 0 0