As far as I can tell, that paper explores a model’s ability to assess its own future text output beyond what “regurgitating training data” would suggest. It makes sense that it could do this better than an outside model; it’s exploring its inner state with each token, though the test is still interesting.
That has nothing to do with sampling, though.
…Maybe an analogy would illustrate this better. First, I don’t mean to anthrophomorphize LLMs, but the human brain is a good example.
The paper is analogous to asking a human brain to assess its own thoughts and tedencies. Of course it can do this well; it can think conscously and run thoughts through its subconscious parts.
What OP is proposing, is analogous to “tell your eye receptors to see less green.” Or “get your vocal cords to omit a certain frequency when you speak.”
There is no wiring in the human brain to do this. Vocal cords and cells that sense green in the eye are effectively “external machinery” to the brain that it does not have such control over.
LLM sampling is the same.
Tokenization and sampling are external machinery. They are code, hardcoded programming, completely outside the LLM weights. You can’t tell an LLM to alter its own sampling because its literally impossible, and it can’t manipulate its own logit spread mathematically because that’s invisible inner machinery.
Could you do this with custom sampler/logit manipulation code and a tool harness?
Sure. Maybe.
But Claude is not rigged to do that, and a system prompt won’t change that.