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Found 4 papers, 1 papers with code
LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70B
Our fine-tuning method retains general performance, which we validate by comparing our fine-tuned models against Llama 2-Chat across two benchmarks.
BadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13B
Llama 2-Chat is a collection of large language models that Meta developed and released to the public.
Constitutional AI: Harmlessness from AI Feedback
In the RL phase, we sample from the finetuned model, use a model to evaluate which of the two samples is better, and then train a preference model from this dataset of AI preferences.
Measuring Progress on Scalable Oversight for Large Language Models
Developing safe and useful general-purpose AI systems will require us to make progress on scalable oversight: the problem of supervising systems that potentially outperform us on most skills relevant to the task at hand.
