Does anyone have intuition for whether or not anti-censorship fine-tuning can actually reverse the performance damage of lobotomization or does the perf hit remain even after the model is free of its straight jacket?
That's not how it works. Llama and Llama 2's raw model is not "censored". Their fine tunes often are, either explicitly, like Facebook's own chat fine tune of llama 2, or inadvertently, because they trained with data derived from chatGPT, and chatGPT is "censored".
When models are "uncensored", people are just tweaking the data used for fine tuning and training the raw models on it again.
> because they trained with data derived from chatGPT
Can you expand on this (genuinely curious)? Did Facebook use ChatGPT during the fine-tuning process for llama, or are you referring to independent developers doing their own fine-tuning of the models?
These "uncensored" models are themselves chat-tuned derivatives of the base models. There is no censorship-caused lobotomization to reverse in this case.
Although, chat tuning in general, censored or uncensored, also decreases performance in many domains. LLMs are better used as well-prompted completion engines than idiot-proof chatbots.
For that reason, I stick to the base models as much as possible. (Rest in peace, code-davinci-002, you will be missed.)
You don't really need to reverse anything in the case of Llama 2. You can just finetune their base model with any open instruct dataset (which is largely what the community is doing).
Does anyone have intuition for whether or not anti-censorship fine-tuning can actually reverse the performance damage of lobotomization or does the perf hit remain even after the model is free of its straight jacket?