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Make your prompts 5x more interpretable without hurting accuracy: IPL combines discrete token selection with continuous optimization, and it's plug-and-play with existing methods.
Editing a rule in an LLM is not like editing a fact; you can't just tweak one layer – formulas live in early layers, instances in the middle.
Overcome the limitations of prompt learning with partial labels: a novel approach that leverages both local feature structure and global distribution alignment to select optimal labels.
