c2023-ice-prompt-based-correction

Summary

ICE corrects a model's knowledge by carefully crafting the input prompt to override what the model would otherwise say, rather than actually altering the model's internal weights. This matters because the fix is temporary and prompt-dependent — the model's underlying training is untouched, so the correction only holds while that specific prompting setup is in place, making it cheap and reversible but also fragile.