The previous paper on self correction told the model "you previously said X - are there errors with this?"
This one has the mistakes statically added to the prompt in a task prompt and response without additional context immediately before asking if it has any errors.
Think about the training data.
How often does the training data of most of the Internet reflect users identifying issues with their own output?
How often does the training data reflect users identifying issues with someone else's output?
Try doing self-correction by setting up the context of "this was someone else's answer". It is still technically self-correction if a model is reviewing its own output in that context - it just isn't set up as "correct your own answer."
This may even be part of why the classifier did a better job at identifying issues - less the fine tuning and more the context (unfortunately I don't see the training/prompts for the classifier in their GitHub repo).
It really seems like the aversion to anthropomorphizing LLMs is leading people to ignore or overlook relevant patterns in the highly anthropomorphic training data fed into them. We might not want to entertain that a LLM has a concept of self vs other or a bias between critiques based on such a differentiation, and yet the training data almost certainly reflects such a concept and bias.
I'd strongly encourage future work on self-correction to explicitly define the thing being evaluated as the work of another. (Or ideally even compare self-correction rates between critiques in the context of their own output vs another's output.)
That's the point: The internet IS full of pedants correcting others' statements. (Hopefully those pedants are right enough of the time for this to be helpful training data, heh.)
I think GP (kromem) was pointing out that those corrections are more likely to be phrased as "You're wrong, here's why..." than as "I'm sorry, I was mistaken" because humans are full of sass for other humans and not as full of first-person admitted errors.