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J-Miner Extracts Executable Decision Knowledge from LLM Classifiers

Yunfan Gao, Xinyi Huang, Tao Sheng, Haorui Song, Yun Xiong, Haofen Wang· August 19, 2026 View original

Key takeaways

  • J-Miner extracts implicit decision knowledge from LLM classifiers into explicit rules.
  • It identifies 'named concepts' and learns executable decision rules over them.
  • The extracted rules achieve high fidelity to the source classifier's decisions.
  • Knowledge transfers effectively to lightweight student models, maintaining accuracy with fewer parameters.

Who benefits

Customer ServiceLegalHealthcareFinanceContent Moderation

Summary

J-Miner is a new system that recovers implicit decision knowledge from fine-tuned language model classifiers, encoding it into explicit, executable rules. This process distills internal signals into inspectable structures, achieving high fidelity and enabling knowledge transfer to lightweight models.

A new research paper introduces J-Miner, a system designed to make the decision-making processes of fine-tuned large language model (LLM) classifiers transparent. While LLMs excel at complex text classification, their internal reasoning often remains opaque, exposing only the final label. J-Miner addresses this by mining internal decision knowledge. It aggregates vocabulary-aligned signals across model layers and token positions to identify 'named concepts' relevant to the task. Using the classifier's own predictions, it then learns explicit, executable decision rules based on these concepts. This distillation process transforms distributed internal evidence into an inspectable knowledge representation. The system demonstrates remarkable fidelity, reproducing up to 98.3% of the source classifier's decisions and significantly outperforming rules learned from input words. Furthermore, this extracted knowledge can be transferred to lightweight student models, which, despite having 24 times fewer parameters, retain 99.8% of the source classifier's accuracy, enabling efficient and interpretable deployment.

Why it matters

This innovation significantly improves the interpretability and efficiency of LLM classifiers, allowing professionals to understand, validate, and reuse complex decision logic, which is critical for trust and resource optimization.

How to implement this in your domain

  1. 1Evaluate current LLM classifier deployments for interpretability and explainability needs.
  2. 2Pilot J-Miner or similar knowledge extraction techniques on a critical classification task.
  3. 3Develop a framework for validating and refining the extracted executable decision rules.
  4. 4Explore deploying lightweight 'student' models derived from J-Miner for edge or resource-constrained environments.
  5. 5Train data scientists on methods for distilling and interpreting LLM decision knowledge.

Original post by Yunfan Gao, Xinyi Huang, Tao Sheng, Haorui Song, Yun Xiong, Haofen Wang

"arXiv:2608.17063v1 Announce Type: new Abstract: Large language models can be fine-tuned into specialized classifiers that perform well across diverse text tasks and make complex judgments, but they typically expose only final labels, leaving the decision knowledge acquired throug…"

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Originally posted by Yunfan Gao, Xinyi Huang, Tao Sheng, Haorui Song, Yun Xiong, Haofen Wang on X · view source

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