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中文(ZH) 爱思唯尔把Meta告了:拿Sci-Hub盗版论文训练大模型

Elsevier sues Meta over AI training data, citing copyright infringement

Academic publishing giant Elsevier, along with other publishers and authors, has filed a lawsuit against Meta, accusing the company of illegally scraping and using copyrighted research papers to train its Llama large language models. The plaintiffs allege that Meta obtained these papers through the Common Crawl dataset and, more directly, through notorious piracy platforms like LibGen and Sci-Hub. Meta is defending its actions by invoking the 'fair use' doctrine, arguing that using copyrighted material for AI training constitutes transformative use and promotes innovation, citing a previous court ruling that allowed Anthropic to use purchased books for AI training. AI

IMPACT This lawsuit could set a crucial legal precedent for AI training data, potentially impacting how models are developed and the cost of accessing copyrighted materials.

RANK_REASON Major academic publisher Elsevier initiates a lawsuit against Meta, alleging copyright infringement in AI model training, marking a significant legal battle over data usage.

Read on 量子位 (QbitAI) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Elsevier sues Meta over AI training data, citing copyright infringement

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Major academic publisher Elsevier initiates a lawsuit against Meta, alleging copyright infringement in AI model training, marking a significant legal battle over data usage.
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2 independent sources
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policy, product, other
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137 days old
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COVERAGE [2]

  1. 量子位 (QbitAI) TIER_1 中文(ZH) · 西风 ·

    Elsevier sues Meta: using Sci-Hub for pirated paper training of large models

    多方原告组团上阵

  2. Mastodon — mastodon.social TIER_1 English(EN) · hauschke ·

    https://www. nature.com/articles/d41586-026 -01481-0 "first science publisher sues over scraped research papers" # Meta vs. # Elsevier - you really don't know w

    https://www. nature.com/articles/d41586-026 -01481-0 "first science publisher sues over scraped research papers" # Meta vs. # Elsevier - you really don't know who to root for less. # Llama # AI # Copyright