SCOPE: Intrinsic Semantic Space Control for Mitigating Copyright Infringement in LLMs

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM

The recent publication of 'SCOPE: Intrinsic Semantic Space Control for Mitigating Copyright Infringement in LLMs' presents a groundbreaking approach to mitigating copyright risks in large language models (LLMs). Traditional methods often depend on surface-level token matching and external filters, which can complicate deployment and fail to address semantically paraphrased content. SCOPE, however, utilizes a sparse autoencoder (SAE) to project hidden states into a high-dimensional semantic space, allowing for the identification and clamping of copyright-sensitive activations during decoding. Experimental results demonstrate that SCOPE effectively reduces copyright infringement while maintaining the general utility of LLMs. This innovation is crucial as it not only enhances the legal safety of AI applications but also ensures that the performance of these models remains intact, marking a significant step forward in the responsible development of AI technologies.

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