Evaluating The Openness of Impactful AI Models with A Focus on LLMs 


Vol. 14,  No. 6, pp. 468-479, Jun.  2025
https://doi.org/10.3745/TKIPS.2025.14.6.468


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  Abstract

Generative AI models are increasingly driving technical innovations and making societal impacts. Recognizing their significance, governments(e.g, EU AI Act) and technical communities (e.g., OSI) demand a higher degree of openness and transparency in AI development. Open sourcing AI models enables deeper scrutiny of their inner workings, accelerates innovation, and mitigates potential risks. Although numerous AI models are marketed as “open source,” many fall short of the traditional standards of openness. Moreover, despite recent efforts, there is currently no comprehensive framework for characterizing the openness of AI models. In this paper, we propose a novel framework to quantify the degree of openness in AI models. We apply our framework to evaluate several high-impact models, including models developed by Korean companies, and investigate the relationship between openness and performance of AI models with a focus on large language models (LLMs).

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[IEEE Style]

K. Jeon, H. Han, K. Lee, "Evaluating The Openness of Impactful AI Models with A Focus on LLMs," The Transactions of the Korea Information Processing Society, vol. 14, no. 6, pp. 468-479, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.6.468.

[ACM Style]

Kil-Won Jeon, Hyeon-jun Han, and Kang-Won Lee. 2025. Evaluating The Openness of Impactful AI Models with A Focus on LLMs. The Transactions of the Korea Information Processing Society, 14, 6, (2025), 468-479. DOI: https://doi.org/10.3745/TKIPS.2025.14.6.468.

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