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How does this helps with making a LLM write in a particular style present in a large corpus? Is there a training step? Or does SRT can use the raw data as is? (seems unfeasible)

Also is SRT really suitable for style transfer?

I mean this seems to be another network overlaid on top of the LLM steering it, but it needs some target to determine whether the underlying LLM drifted away from it

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SRT does involve a training step, but only on the small adapter and not on the base model. It learns to shift internal representations toward a target discourse regime or style.

It is an overlay, but it works by modulating meaning level patterns called regimes rather than fixed steering vectors. Because it can read its own effect on the hidden states it gives a way to observe whether output is staying in the target regime or drifting.

It is not raw data in and raw style out. The adapter needs examples that define the desired regime.

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