Author

Tao Sheng

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Preprint Aug 2026

J-Miner: Recovering the Decision Logic of Fine-Tuned LLM Classifiers as Compact Rules

Task-fine-tuned large language model (LLM) classifiers acquire task-specific decision knowledge, but this knowledge remains implicit in distributed internal computations, making their decision logic difficult to interpret. We introduce the Executable Decision Compression (EDC) framework and propose J-Miner, which mines...

Yun-Fan Gao, Xinyi Huang, Tao Sheng et al. · 0 citations

#machine learning Preprint Aug 2026

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 e...

Yunfan Gao, Xinyi Huang, Tao Sheng et al. · 0 citations