DKnown-Jev: Fine-Tuning and Evaluation of a JEV-Style Model for Tasks Involving Normative Knowledge

Qi Li, Jianjun Xu, Pingtao Wei

Tasks involving normative knowledge include concept interpretation, eligibility determination, procedure explanation, material requirements, and service-outcome assessment, all of which require rules and facts published, maintained, or recorded by authorized entities. Whether a candidate paragraph can answer a question therefore depends on applicability and answer completeness as well as topical relevance. We develop DKnown-Jev to classify the normative usability of candidate paragraphs into two classes. The 2B and 27B configurations are fine-tuned directly from the native Qwen3.5-2B and Qwen3.8-27B base models, respectively. The models combine question conditions, article metadata, and paragraph content, adopt the classes Answer Evidence (AE) and Non-answer Evidence (NE), and undergo domain adaptation using human-verified training data. Five configurations are evaluated on the same test set of 1,120 human-verified examples. DKnown-Jev 27B trained for one epoch achieves the highest accuracy of 98.75% and macro-F1 of 98.60%, exceeding Jev by 6.07 and 6.76 percentage points, respectively. DKnown-Jev 2B trained for one epoch achieves 92.86% accuracy and 92.18% macro-F1, the best…