LoRA: Low-Rank Adaptation of Large Language Models
Low-rank trainable updates adapt a frozen pretrained model without updating all of its parameters.
Models assess their own conversations as an additional misuse-detection layer, reusing cached context to limit overhead.
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Compare self-assessment with independent classifiers; detection is not a guarantee of safe behavior.
@misc{zhang2026scaling,
title = {Scaling Misuse Detection with Self-Assessment},
author = {Yiming Zhang},
year = {2026},
url = {https://safeguards.anthropic.com/2026/self-assessment/}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
Low-rank trainable updates adapt a frozen pretrained model without updating all of its parameters.
Human demonstrations and preference feedback are used to improve how a language model follows instructions.
The study examines how to allocate a training-compute budget between model size and training data.