Summary of Copyright-protected Language Generation Via Adaptive Model Fusion, by Javier Abad et al.
Copyright-Protected Language Generation via Adaptive Model Fusion
by Javier Abad, Konstantin Donhauser, Francesco Pinto, Fanny Yang
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Copyright-Protecting Model Fusion (CP-Fuse), a novel approach to prevent language models from reproducing copyrighted material during inference. By combining models trained on disjoint sets of copyrighted content and adaptively aggregating their outputs, CP-Fuse minimizes the reproduction of protected material while preserving text and code generation quality. The approach also ensures seamless integration with other protective measures and is robust against common techniques for extracting training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to protect language models from reproducing copyrighted material by introducing a new approach called Copyright-Protecting Model Fusion (CP-Fuse). CP-Fuse combines models trained on different pieces of copyrighted content during inference, which helps prevent the model from repeating what it was trained on. This keeps the output original and unique. |
Keywords
» Artificial intelligence » Inference
