mlsanityData to 14 Oct 2026

Project Alexandria: Towards Freeing Scientific Knowledge from Copyright Burdens via LLMs

arXiv 2502.19413 ↗First posted 26 Feb 2025cs.LG cs.AI cs.CLcited by 3

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Abstract (arXiv)

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 propose a new idea for the community to adopt: convert scholarly documents into knowledge preserving, but style agnostic representations we term 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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 8 of its 40 references that are in the map. First posted on arXiv 26 Feb 2025.

Builds on

8 of 40 references.

References · 8

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
  4. 2022MTEB: Massive Text Embedding Benchmarkcited by 640
  5. 2020Knowledge Graphscited by 621
  6. 2018A Discourse-Aware Attention Model for Abstractive Summarization of Long Documentscited by 478
  7. 2019On the Use of ArXiv as a Datasetcited by 103
  8. 2021No Deal: Investigating the Influence of Restricted Access to Elsevier Journals on German Researchers' Publishing and Citing Behaviourscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.