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

Key points

• 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.1
• Whether a candidate paragraph can answer a question therefore depends on applicability and answer completeness as well as topical relevance.1
• 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.1
• This study provides practical and empirical evidence for developing and applying candidate-scoring models to tasks involving normative knowledge.1

Sources (1)

• [1]DKnown-Jev: Fine-Tuning and Evaluation of a JEV-Style Model for Tasks Involving Normative Knowledge
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 01:17 AM

  We develop DKnown-Jev to classify the normative usability of candidate paragraphs into two classes.

  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.

Extractive summary: sentences quoted from the sources.