Development of REGAI: Rubric Enabled Generative Artificial Intelligence

2024/08/05 by Zach Johnson, Johnson, Zach, Jeremy Straub +1
Computer Science · Decision Sciences · Psychology · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Artificial intelligence #Cognitive science #Computer science #FOS: Computer and information sciences #Generative grammar #Machine learning #Mathematics education #Psychology #Rubric #Scientific Computing and Data Management #Semantic Web and Ontologies

paper · pdf · doi:10.48550/arxiv.2408.02811

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2024/08/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper presents and evaluates a new retrieval augmented generation (RAG) and large language model (LLM)-based artificial intelligence (AI) technique: rubric enabled generative artificial intelligence (REGAI). REGAI uses rubrics, which can be created manually or automatically by the system, to enhance the performance of LLMs for evaluation purposes. REGAI improves on the performance of both classical LLMs and RAG-based LLM techniques. This paper describes REGAI, presents data regarding its performance and discusses several possible application areas for the technology.

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