mlsanityData to 7 Oct 2026

Leveraging In-Context Learning and Retrieval-Augmented Generation for Automatic Question Generation in Educational Domains

arXiv 2501.17397 ↗First posted 29 Jan 2025cs.CLcited by 1

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

Question generation in education is a time-consuming and cognitively demanding task, as it requires creating questions that are both contextually relevant and pedagogically sound. Current automated question generation methods often generate questions that are out of context. In this work, we explore advanced techniques for automated question generation in educational contexts, focusing on In-Context Learning (ICL), Retrieval-Augmented Generation (RAG), and a novel Hybrid Model that merges both methods. We implement GPT-4 for ICL using few-shot examples and BART with a retrieval module for RAG. The Hybrid Model combines RAG and ICL to address these issues and improve question quality. Evaluation is conducted using automated metrics, followed by human evaluation metrics. Our results show that both the ICL approach and the Hybrid Model consistently outperform other methods, including baseline models, by generating more contextually accurate and relevant questions.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 21 of its 47 references that are in the map. First posted on arXiv 29 Jan 2025.

Builds on

21 of 47 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering6 Oct

Other references · 20

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  3. 2017Billion-scale similarity search with GPUscited by 2,477
  4. 2022Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learningcited by 886
  5. 2023Active Retrieval Augmented Generationcited by 506
  6. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 450
  7. 2020A Simple Language Model for Task-Oriented Dialoguecited by 331
  8. 2023ToolQA: A Dataset for LLM Question Answering with External Toolscited by 273
  9. 2022In-context Examples Selection for Machine Translationcited by 172
  10. 2020Re-evaluating Evaluation in Text Summarizationcited by 144
  11. 2024Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answeringcited by 95
  12. 2024Evaluating Retrieval Quality in Retrieval-Augmented Generationcited by 90
8 more
  1. 2020Retrieval-Augmented Generation for Code Summarization via Hybrid GNNcited by 89
  2. 2018Towards a Better Metric for Evaluating Question Generation Systemscited by 52
  3. 2020Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answeringcited by 39
  4. 2023In-Context Learning for Text Classification with Many Labelscited by 35
  5. 2023On Context Utilization in Summarization with Large Language Modelscited by 30
  6. 2022Generative Language Models for Paragraph-Level Question Generationcited by 28
  7. 2024A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPTcited by 19
  8. 2023Neural Machine Translation of Clinical Text: An Empirical Investigation into Multilingual Pre-Trained Language Models and Transfer-Learningcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Potential for Enhanced Learning in Machine Learning Classes by Using Wiki LLM Indexingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects
  2. 2024Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questions
  3. 2025RAG-RL: Advancing Retrieval-Augmented Generation via RL and Curriculum Learning
  4. 2024Context Awareness Gate For Retrieval Augmented Generation
  5. 2024In Defense of RAG in the Era of Long-Context Language Models
  6. 2025Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational Videos
  7. 2024Enhancing textual textbook question answering with large language models and retrieval augmented generation
  8. 2025Generate-Then-Validate: A Novel Question Generation Approach Using Small Language Models