mlsanityData to 7 Oct 2026

A Fine-tuning Enhanced RAG System with Quantized Influence Measure as AI Judge

arXiv 2402.17081 ↗First posted 26 Feb 2024cs.IRcited by 9

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

This study presents an innovative enhancement to retrieval-augmented generation (RAG) systems by seamlessly integrating fine-tuned large language models (LLMs) with vector databases. This integration capitalizes on the combined strengths of structured data retrieval and the nuanced comprehension provided by advanced LLMs. Central to our approach are the LoRA and QLoRA methodologies, which stand at the forefront of model refinement through parameter-efficient fine-tuning and memory optimization. A novel feature of our research is the incorporation of user feedback directly into the training process, ensuring the model's continuous adaptation to user expectations and thus, improving its performance and applicability. Additionally, we introduce a Quantized Influence Measure (QIM) as an innovative "AI Judge" mechanism to enhance the precision of result selection, further refining the system's accuracy. Accompanied by an executive diagram and a detailed algorithm for fine-tuning QLoRA, our work provides a comprehensive framework for implementing these advancements within chatbot technologies. This research contributes significant insights into LLM optimization for specific uses and heralds new directions for further development in retrieval-augmented models. Through extensive experimentation and analysis, our findings lay a robust foundation for future advancements in chatbot technology and retrieval systems, marking a significant step forward in the creation of more sophisticated, precise, and user-centric conversational AI systems.

Key facts. Cited by 9 papers 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 36 of its 69 references that are in the map. First posted on arXiv 26 Feb 2024.

Builds on

36 of 69 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec
  2. 2022A Survey on Retrieval-Augmented Text Generation2 Feb

Other references · 34

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  4. 2020Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuningcited by 658
  5. 2023Active Retrieval Augmented Generationcited by 506
  6. 2023Benchmarking Large Language Models in Retrieval-Augmented Generationcited by 307
  7. 2023FinGPT: Open-Source Financial Large Language Modelscited by 244
  8. 2023OpenAGI: When LLM Meets Domain Expertscited by 196
  9. 2023LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Modelscited by 195
  10. 2020Generation-Augmented Retrieval for Open-domain Question Answeringcited by 186
  11. 2023QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Modelscited by 154
  12. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answeringcited by 126
22 more
  1. 2023Large Language Models as Zero-Shot Conversational Recommenderscited by 126
  2. 2023LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model Finetuningcited by 92
  3. 2020Retrieval-Augmented Generation for Code Summarization via Hybrid GNNcited by 89
  4. 2023To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisiscited by 88
  5. 2024Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learningcited by 80
  6. 2023EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Modelscited by 77
  7. 2023LLMs4OL: Large Language Models for Ontology Learningcited by 75
  8. 2023Delta-LoRA: Fine-Tuning High-Rank Parameters with the Delta of Low-Rank Matricescited by 72
  9. 2022Retrieval-Augmented Reinforcement Learningcited by 53
  10. 2024An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Projectcited by 28
  11. 2022Retrieval augmentation of large language models for lay language generationcited by 21
  12. 2023Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Modelcited by 18
  13. 2023Integrating Graphs with Large Language Models: Methods and Prospectscited by 18
  14. 2022Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Trainingcited by 16
  15. 2023An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approachcited by 12
  16. 2023The Poison of Alignmentcited by 10
  17. 2023Guiding LLM to Fool Itself: Automatically Manipulating Machine Reading Comprehension Shortcut Triggerscited by 10
  18. 2024Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Modelscited by 9
  19. 2023ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizerscited by 6
  20. 2021A Novel Interaction-based Methodology Towards Explainable AI with Better Understanding of Pneumonia Chest X-ray Imagescited by 2
  21. 2023LMTuner: An user-friendly and highly-integrable Training Framework for fine-tuning Large Language Modelscited by 2
  22. 2021Language Semantics Interpretation with an Interaction-based Recurrent Neural Networkscited by 1

Built on by

Cited by year

Citing papers per year: 2024: 5; 2025: 4.

9 papers.

  1. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
  2. 2024Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and Scalabilitycited by 15
  3. 2024GPT vs RETRO: Exploring the Intersection of Retrieval and Parameter-Efficient Fine-Tuningcited by 4
  4. 2024Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Modelscited by 4
  5. 2025QuOTE: Question-Oriented Text Embeddingscited by 2
  6. 2025Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codescited by 2
  7. 2024Efficient Learning Content Retrieval with Knowledge Injectioncited by 0
  8. 2025FAIRTOPIA: Envisioning Multi-Agent Guardianship for Disrupting Unfair AI Pipelinescited by 0
  9. 2025Text Production and Comprehension by Human and Artificial Intelligence: Interdisciplinary Workshop Reportcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025A Knowledge Graph and a Tripartite Evaluation Framework Make Retrieval-Augmented Generation Scalable and Transparent
  2. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
  3. 2024Evaluating the Retrieval Component in LLM-Based Question Answering Systems
  4. 2024The Power of Noise: Redefining Retrieval for RAG Systems
  5. 2025Comparing the Performance of LLMs in RAG-based Question-Answering: A Case Study in Computer Science Literature
  6. 2026Evaluating Multi-Hop Reasoning in RAG Systems: A Comparison of LLM-Based Retriever Evaluation Strategies
  7. 2024ERATTA: Extreme RAG for Table To Answers with Large Language Models
  8. 2024VERA: Validation and Evaluation of Retrieval-Augmented Systems