Development of REGAI: Rubric Enabled Generative Artificial Intelligence
On the map: Retrieval-augmented generation → RAG pipeline design and evaluation
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 26 of its 46 references that are in the map. First posted on arXiv 5 Aug 2024.
Builds on
26 of 46 references.
References · 26
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2020Measuring Massive Multitask Language Understandingcited by 7,768
- 2017Deep reinforcement learning from human preferencescited by 3,846
- 2020Learning to summarize from human feedbackcited by 2,562
- 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
- 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
- 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
- 2019SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systemscited by 1,718
14 more
- 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
- 2018Model Cards for Model Reportingcited by 1,068
- 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
- 2019Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inferencecited by 864
- 2018e-SNLI: Natural Language Inference with Natural Language Explanationscited by 468
- 2018Analysis Methods in Neural Language Processing: A Surveycited by 276
- 2019SemEval-2017 Task 4: Sentiment Analysis in Twittercited by 209
- 2019Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practicescited by 170
- 2018Neural Approaches to Conversational AIcited by 164
- 2022On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?cited by 137
- 2024Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generationcited by 63
- 2020Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Textcited by 59
- 2018End-to-End Content and Plan Selection for Data-to-Text Generationcited by 20
- 2024Facilitating Holistic Evaluations with LLMs: Insights from Scenario-Based Experimentscited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2024Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report
- 2023A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture
- 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
- 2025On the Merits of LLM-Based Corpus Enrichment ↗
- 2024Retrieval-Augmented Generation for AI-Generated Content: A Survey
- 2024The Power of Noise: Redefining Retrieval for RAG Systems
- 2024VERA: Validation and Evaluation of Retrieval-Augmented Systems
- 2024ERATTA: Extreme RAG for Table To Answers with Large Language Models