Jan 1, 2024 · Title:Digger: Detecting Copyright Content Mis-usage in Large Language Model Training. Authors:Haodong Li, Gelei Deng, Yi Liu, Kailong Wang ...
Building upon this idea, we propose Digger, a framework designed to discern material usage during an LLM's training. Central to our approach is the “loss gap”—a ...
Digger: Detecting Copyright Content Mis-usage in Large Language Model Training. @article{Li2024DiggerDC, title={Digger: Detecting Copyright Content Mis-usage ...
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Jan 1, 2024 · Digger: Detecting Copyright Content Mis-usage in Large Language Model Training. Pre-training, which utilizes extensive and varied datasets ...
Digger: Detecting Copyright Content Mis-usage in Large Language Model Training ... Large Language Models: Categorization Taxonomy and Effective Detection. Y Li, Y ...
Jan 1, 2024 · ... Large Language Models (LLMs) across numerous applications ... Digger: Detecting Copyright Content Mis-usage in Large Language Model Training
Feb 19, 2024 · It's simply models trained on work without permission used for profit instead of research and overall scientific / personal learning and ...
Missing: Digger: Detecting Mis- Language
... Digger to detect the similar groups in the large graphs. People participate in ... Digger: Detecting Copyright Content Mis-usage in Large Language Model Training.
Sep 30, 2023 · Can it be considered as a legal evidence that the copyrighted text was used to train the model? On the premise that the ownership of the ...
Missing: Digger: Mis-
Jan 23, 2024 · Along with other allegations, the New York Times claims that Microsoft and OpenAI are infringing copyright when they train their large language ...
Missing: Digger: Detecting Mis-