title: Project Alexandria: Towards Freeing Scientific Knowledge from Copyright
    Burdens via LLMs
summary: Paywalls, licenses and copyright rules often restrict the broad dissemination
  and reuse of scientific knowledge. We take the position that it is both legally
  and technically feasible to extract the scientific knowledge in scholarly
  texts. Current methods, like text embeddings, fail to reliably preserve factual
  content, and simple paraphrasing may not be legally sound. We urge the
  community to adopt a new idea: convert scholarly documents into Knowledge Units
  using LLMs. These units use structured data capturing entities, attributes and
  relationships without stylistic content. We provide evidence that Knowledge
  Units: (1) form a legally defensible framework for sharing knowledge from
  copyrighted research texts, based on legal analyses of German copyright law and
  U.S. Fair Use doctrine, and (2) preserve most (~95%) factual knowledge from
  original text, measured by MCQ performance on facts from the original
  copyrighted text across four research domains. Freeing scientific knowledge
  from copyright promises transformative benefits for scientific research and
  education by allowing language models to reuse important facts from copyrighted
  text. To support this, we share open-source tools for converting research
  documents into Knowledge Units. Overall, our work posits the feasibility of
  democratizing access to scientific knowledge while respecting copyright.
paper_authors._id: 67c02d6aa15ac71dcf1c754e
paper_authors.name: Christoph Schuhmann
paper_authors._id: 67c02d6aa15ac71dcf1c754f
paper_authors.name: Gollam Rabby
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-27T12:54:33.105Z
paper_authors._id: 64ac21f11cacea8d4b8f2b3f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64ac21f11cacea8d4b8f2b3f/asQOf8wFZ4vmqIeyxfvUR.jpeg
paper_authors.fullname: Gollam Rabby
paper_authors.type: user
paper_authors.user: tourist800
paper_authors._id: 67c02d6aa15ac71dcf1c7550
paper_authors.name: Ameya Prabhu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-27T12:54:05.760Z
paper_authors._id: 6464a0d41683d3c81f51924a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6464a0d41683d3c81f51924a/s7yYVwfUB4WOhVFJS6A6T.jpeg
paper_authors.fullname: Ameya Prabhu
paper_authors.type: user
paper_authors.user: AmeyaPrabhu
paper_authors._id: 67c02d6aa15ac71dcf1c7551
paper_authors.name: Tawsif Ahmed
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-27T22:09:11.253Z
paper_authors._id: 635b9bc5cb0f36a40bb43ee3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/635b9bc5cb0f36a40bb43ee3/SVe1ZvCIfNlYdpWJ35Nu0.jpeg
paper_authors.fullname: tawsif
paper_authors.type: user
paper_authors.user: sleeping4cat
paper_authors._id: 67c02d6aa15ac71dcf1c7552
paper_authors.name: Andreas Hochlehnert
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-27T22:09:08.923Z
paper_authors._id: 64ff3944f0d65cca9b867ed2
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64ff3944f0d65cca9b867ed2/jWnHkF4AUzh51MkC0UT6b.png
paper_authors.fullname: Andreas Hochlehnert
paper_authors.type: user
paper_authors.user: libeanim
paper_authors._id: 67c02d6aa15ac71dcf1c7553
paper_authors.name: Huu Nguyen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-02T20:18:16.174Z
paper_authors._id: 5fc6879e1c5ee87b1164876d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/5fc6879e1c5ee87b1164876d/Tjnm_lv0Bq0gPbFOTDH6E.jpeg
paper_authors.fullname: Huu Nguyen
paper_authors.type: user
paper_authors.user: huu-ontocord
paper_authors._id: 67c02d6aa15ac71dcf1c7554
paper_authors.name: Nick Akinci Heidrich
paper_authors._id: 67c02d6aa15ac71dcf1c7555
paper_authors.name: Ludwig Schmidt
paper_authors._id: 67c02d6aa15ac71dcf1c7556
paper_authors.name: Robert Kaczmarczyk
paper_authors._id: 67c02d6aa15ac71dcf1c7557
paper_authors.name: Sören Auer
paper_authors._id: 67c02d6aa15ac71dcf1c7558
paper_authors.name: Jenia Jitsev
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-28T12:15:55.422Z
paper_authors._id: 6355b485b8b79340d4630dd5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6355b485b8b79340d4630dd5/HIZO4ybweRy48VdCtk2MB.jpeg
paper_authors.fullname: Jenia Jitsev
paper_authors.type: user
paper_authors.user: JJitsev
paper_authors._id: 67c02d6aa15ac71dcf1c7559
paper_authors.name: Matthias Bethge
paper_title: Project Alexandria: Towards Freeing Scientific Knowledge from Copyright
    Burdens via LLMs
paper_summary: Paywalls, licenses and copyright rules often restrict the broad dissemination
  and reuse of scientific knowledge. We take the position that it is both legally
  and technically feasible to extract the scientific knowledge in scholarly
  texts. Current methods, like text embeddings, fail to reliably preserve factual
  content, and simple paraphrasing may not be legally sound. We urge the
  community to adopt a new idea: convert scholarly documents into Knowledge Units
  using LLMs. These units use structured data capturing entities, attributes and
  relationships without stylistic content. We provide evidence that Knowledge
  Units: (1) form a legally defensible framework for sharing knowledge from
  copyrighted research texts, based on legal analyses of German copyright law and
  U.S. Fair Use doctrine, and (2) preserve most (~95%) factual knowledge from
  original text, measured by MCQ performance on facts from the original
  copyrighted text across four research domains. Freeing scientific knowledge
  from copyright promises transformative benefits for scientific research and
  education by allowing language models to reuse important facts from copyrighted
  text. To support this, we share open-source tools for converting research
  documents into Knowledge Units. Overall, our work posits the feasibility of
  democratizing access to scientific knowledge while respecting copyright.
paper_ai_summary: Using language models to convert scholarly documents into structured Knowledge Units can legally and effectively preserve and disseminate scientific knowledge.