Tao Sheng

J-Miner: Recovering Executable Decision Knowledge from Language-Model Classifiers

J-Miner is introduced, which mines text-level named concepts by aggregating vocabulary-aligned internal signals across layers and token positions, and uses the classifier's own predictions to learn executable decision rules over them, and shows that task-specific decision knowledge can be faithfully represented in an explicit, executable form and reused beyond the classifier in which it was learned.