arXiv:2602.07120v2 Announce Type: replace Abstract: Language models (LMs) tend to memorize portions of their training data and emit verbatim spans. When the underlying sources are sensitive or copyright-protected, such reproduction raises issues of consent and compensation for creators and compliance risks for developers. We propose Anchored Decoding, a plug-and-play inference-time method for suppressing verbatim copying: it enables decoding from any risky LM trained on mixed-license data by keeping generation in bounded proximity to a permissively trained safe LM. Anchored Decoding adaptively
The proliferation of powerful large language models and increasing legal scrutiny on their training data and outputs necessitate immediate solutions for copyright compliance.
This development offers a potential 'plug-and-play' solution for mitigating copyright infringement risks in AI-generated content, crucial for safe and ethical AI deployment.
Developers can now deploy risky LMs trained on diverse data with a provable method to suppress verbatim copying, reducing legal exposure and expanding application possibilities.
- · AI developers and deployers
- · Language Model users
- · Legal tech firms
- · Content creators (through increased compliance)
- · Entities seeking to exploit copyright loopholes
- · Competitors without similar mitigating technologies
Widespread adoption of Anchored Decoding could significantly reduce copyright lawsuits against AI models.
This might encourage more aggressive training data aggregation, as risks are mitigated post-training.
The development of a 'safe' foundation model market could emerge, influencing future LM architecture and investment.
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Read at arXiv cs.CL