Researchers have developed a method called J-Miner to extract decision-making knowledge from large language models. These models can be fine-tuned for specific tasks and make complex judgments, but the process of making these decisions is not transparent. J-Miner mines internal signals within the model to create an executable representation of the decision-making process. This representation can be inspected, validated, and reused in other applications. The researchers tested J-Miner on multiple classification tasks and found that it was able to reproduce up to 98.3% of the source-classifier decisions and achieve higher behavioral fidelity than rules learned from input words. The extracted knowledge also transferred well to lightweight standalone models, retaining 99.8% of the source classifiers' mean task accuracy. --- Why it matters: This matters because it shows that complex decision-making processes in language models can be made transparent and reusable. This could have significant implications for applications where understanding how decisions are made is crucial, such as in healthcare or finance. Source: https://arxiv.org/abs/2608.17063