arXiv:2606.18767v1 Announce Type: new Abstract: Large language models memorize and reproduce sequences from their training data, creating privacy, copyright, and security risks. Existing neuron-level mitigation methods equate editing with zeroing out neuron activations, but the activation only controls whether a neuron engages; the output vector is what writes to the residual stream and, through superposition, encodes multiple features. We propose output vector editing, a constrained-optimization weight edit that locates a small set of MLP neurons responsible for a memorized continuation and m
The increasing scale and deployment of large language models are highlighting critical issues around data privacy, copyright, and security, creating urgency for mitigation techniques.
Addressing memorization in LLMs is crucial for their responsible and widespread adoption, impacting trust, legal compliance, and the development of safer AI systems.
The ability to precisely edit specific memorized content within LLMs without broad model retraining offers a more efficient and targeted approach to mitigate risks.
- · AI developers
- · Enterprises using LLMs
- · Data privacy advocates
- · Users of large language models
- · Malicious actors exploiting memorization
- · Models with unmitigated memorization issues
Reduced legal and ethical risks associated with deploying foundational large language models.
Increased commercialization and integration of LLMs into sensitive applications, broadening their utility.
Potential for new regulations and industry standards around AI model editing and data provenance.
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