mlsanityData to 14 Oct 2026

Efficient Learning Content Retrieval with Knowledge Injection

arXiv 2412.00125 ↗First posted 28 Nov 2024cs.CLcited by 0

On the map: Retrieval-augmented generation → Agentic RAG, agents and memory

Abstract (arXiv)

With the rise of online education platforms, there is a growing abundance of educational content across various domain. It can be difficult to navigate the numerous available resources to find the most suitable training, especially in domains that include many interconnected areas, such as ICT. In this study, we propose a domain-specific chatbot application that requires limited resources, utilizing versions of the Phi language model to help learners with educational content. In the proposed method, Phi-2 and Phi-3 models were fine-tuned using QLoRA. The data required for fine-tuning was obtained from the Huawei Talent Platform, where courses are available at different levels of expertise in the field of computer science. RAG system was used to support the model, which was fine-tuned by 500 Q&A pairs. Additionally, a total of 420 Q&A pairs of content were extracted from different formats such as JSON, PPT, and DOC to create a vector database to be used in the RAG system. By using the fine-tuned model and RAG approach together, chatbots with different competencies were obtained. The questions and answers asked to the generated chatbots were saved separately and evaluated using ROUGE, BERTScore, METEOR, and BLEU metrics. The precision value of the Phi-2 model supported by RAG was 0.84 and the F1 score was 0.82. In addition to a total of 13 different evaluation metrics in 4 different categories, the answers of each model were compared with the created content and the most appropriate method was selected for real-life applications.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation. Builds on 30 of its 41 references that are in the map. First posted on arXiv 28 Nov 2024.

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30 of 41 references.

References · 30

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  5. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  6. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  7. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  8. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  9. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  10. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  11. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
  12. 2023Mistral 7Bcited by 3,164
18 more
  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  2. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  3. 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,917
  4. 2023A Survey on Evaluation of Large Language Modelscited by 1,242
  5. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
  6. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  7. 2023Textbooks Are All You Need II: phi-1.5 technical reportcited by 565
  8. 2024Evaluation of Retrieval-Augmented Generation: A Surveycited by 146
  9. 2024Exploiting LLM Quantizationcited by 65
  10. 2024MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learningcited by 44
  11. 2024Fine-tuning and Utilization Methods of Domain-specific LLMscited by 36
  12. 2024Fine Tuning LLM for Enterprise: Practical Guidelines and Recommendationscited by 28
  13. 2023AI-TA: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source LLMscited by 22
  14. 2024Evaluation of LLM Chatbots for OSINT-based Cyber Threat Awarenesscited by 14
  15. 2021A Survey on Legal Question Answering Systemscited by 10
  16. 2024A Fine-tuning Enhanced RAG System with Quantized Influence Measure as AI Judgecited by 9
  17. 2021Fine-tune the Entire RAG Architecture (including DPR retriever) for Question-Answeringcited by 7
  18. 2023Customising General Large Language Models for Specialised Emotion Recognition Taskscited by 7

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