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    <guid isPermaLink="false">https://blog.genlaw.org/papers.html#licensing-training-data-and-attributing-copyright-of-derivative-content-from-large-language-models-can-resolve-up--and-downstream-copyright-issues#licensing-training-data-and-attributing-copyright-of-derivative-content-from-large-language-models-can-resolve-up--and-downstream-copyright-issues-icml-workshop-on-generative-ai-and-the-law</guid>
    <title>Licensing Training Data and Attributing Copyright of Derivative Content From Large Language Models Can Resolve Up- and Downstream Copyright Issues</title>
    <link>https://blog.genlaw.org/papers.html#licensing-training-data-and-attributing-copyright-of-derivative-content-from-large-language-models-can-resolve-up--and-downstream-copyright-issues</link>
    <description>We propose an opt-in system with multimodal similarity measures and metadata tagging for fair IP compensation and attribution, resolving copyright disputes over LLM-generated content.</description>
    <pubDate>Sun, 01 Jan 2023 00:00:00 GMT</pubDate>
    <author>hello@zhoubrian.com (Brian Zhou)</author>
    <category>Publications</category><category>ICML Workshop on Generative AI and the Law</category>
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