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    <abstract>Licenses are legal instruments that inventors rely upon to govern the diffusion of an invention. The unstructured nature of the ecosystem’s governance, however, has made licenses difficult to compare and thus difficult to enforce at scale—when is one license considered more permissive than the other, and when are their terms incomparable to each other? Currently, there is a growing list of licenses that are introduced and used, yet no systematic way to study their relationships. This matters for platforms such as Hugging Face, GitHub, and the Python Package Index, where developers publish or build upon technologies that each have their own licenses. Using large language models (LLMs) and calibrating with human judgement, we introduce methods for comparing licenses at scale: first, in a pairwise fashion to construct and validate a partial ordering based on permissiveness; and by drawing on existing taxonomies of software licenses. Then, by employing a Bradley–Terry model to efficiently recover the permissiveness structure, we observe a performance loss of ~20%–and attribute this loss to the limitations of feature coverage. The partial ordering coupled with model rationale allows us to trace restrictiveness, while the existing taxonomies allows us to understand license selection as a combination of shared provisions.</abstract>
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%0 Conference Proceedings
%T Structuring license permissiveness from pairwise comparisons
%A Oderinwale, Hamidah
%A Hong, Rachel
%A Atkinson, David
%A Abal, Art
%A Laufer, Benjamin
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goanță, Cătălina
%Y Preoțiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%Y Xu, Shanshan
%S Proceedings of the Natural Legal Language Processing Workshop 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-494-1
%F oderinwale-etal-2026-structuring
%X Licenses are legal instruments that inventors rely upon to govern the diffusion of an invention. The unstructured nature of the ecosystem’s governance, however, has made licenses difficult to compare and thus difficult to enforce at scale—when is one license considered more permissive than the other, and when are their terms incomparable to each other? Currently, there is a growing list of licenses that are introduced and used, yet no systematic way to study their relationships. This matters for platforms such as Hugging Face, GitHub, and the Python Package Index, where developers publish or build upon technologies that each have their own licenses. Using large language models (LLMs) and calibrating with human judgement, we introduce methods for comparing licenses at scale: first, in a pairwise fashion to construct and validate a partial ordering based on permissiveness; and by drawing on existing taxonomies of software licenses. Then, by employing a Bradley–Terry model to efficiently recover the permissiveness structure, we observe a performance loss of ~20%–and attribute this loss to the limitations of feature coverage. The partial ordering coupled with model rationale allows us to trace restrictiveness, while the existing taxonomies allows us to understand license selection as a combination of shared provisions.
%U https://aclanthology.org/2026.nllp-1.9/
%P 76-93

Markdown (Informal)

[Structuring license permissiveness from pairwise comparisons](https://aclanthology.org/2026.nllp-1.9/) (Oderinwale et al., NLLP 2026)

• Structuring license permissiveness from pairwise comparisons (Oderinwale et al., NLLP 2026)

ACL

• Hamidah Oderinwale, Rachel Hong, David Atkinson, Art Abal, and Benjamin Laufer. 2026. Structuring license permissiveness from pairwise comparisons. In Proceedings of the Natural Legal Language Processing Workshop 2026, pages 76–93, Budapest, Hungary. Association for Computational Linguistics.