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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
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