mlsanityData to 7 Oct 2026

Contrastive Learning for Knowledge-Based Question Generation in Large Language Models

arXiv 2409.13994 ↗First posted 21 Sep 2024cs.CL cs.AIcited by 8

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Abstract (arXiv)

With the rapid development of artificial intelligence technology, especially the increasingly widespread application of question-and-answer systems, high-quality question generation has become a key component in supporting the development of these systems. This article focuses on knowledge-based question generation technology, which aims to enable computers to simulate the human questioning process based on understanding specific texts or knowledge bases. In light of the issues of hallucination and knowledge gaps present in large-scale language models when applied to knowledge-intensive tasks, this paper proposes an enhanced question generation method that incorporates contrastive learning. This method utilizes multiple models to jointly mine domain knowledge and uses contrastive learning to guide the model in reducing noise and hallucinations in generation. Experimental results show that by designing prompts containing contrasting examples, the model's performance in question generation improves considerably, particularly when contrasting instructions and examples are used simultaneously, leading to the highest quality of generated questions and improved accuracy. These results demonstrate that the method proposed in this study, which combines contrasting context and chain-of-thought prompts, can effectively improve both the quality and the practicality of question generation.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 14 of its 24 references that are in the map. First posted on arXiv 21 Sep 2024.

Builds on

14 of 24 references.

References · 14

  1. 2024Large Language Models: A Surveycited by 506
  2. 2023On the Planning Abilities of Large Language Models : A Critical Investigationcited by 308
  3. 2023NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Spacecited by 88
  4. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
  5. 2023How Useful are Educational Questions Generated by Large Language Models?cited by 23
  6. 2024Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learningcited by 17
  7. 2024Adaptive Friction in Deep Learning: Enhancing Optimizers with Sigmoid and Tanh Functioncited by 16
  8. 2024A comparative study of generative adversarial networks for image recognition algorithms based on deep learning and traditional methodscited by 12
  9. 2024Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visitscited by 12
  10. 2024Algorithm Research of ELMo Word Embedding and Deep Learning Multimodal Transformer in Image Descriptioncited by 10
  11. 2024Applying Conditional Generative Adversarial Networks for Imaging Diagnosiscited by 9
  12. 2024Text classification optimization algorithm based on graph neural networkcited by 8
2 more
  1. 2024Enhancing Convolutional Neural Networks with Higher-Order Numerical Difference Methodscited by 8
  2. 2024Enhancing Deep Learning with Optimized Gradient Descent: Bridging Numerical Methods and Neural Network Trainingcited by 7

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Cited by year

Citing papers per year: 2024: 8.

8 papers.

  1. 2024Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networkscited by 33
  2. 2024Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithmcited by 11
  3. 2024Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentationcited by 10
  4. 2024Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphscited by 9
  5. 2024Balancing Innovation and Privacy: Data Security Strategies in Natural Language Processing Applicationscited by 8
  6. 2024A Recommendation Model Utilizing Separation Embedding and Self-Attention for Feature Miningcited by 8
  7. 2024Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Studycited by 8
  8. 2024Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Managementcited by 6

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  1. 2024The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models
  2. 2025Generate-Then-Validate: A Novel Question Generation Approach Using Small Language Models
  3. 2024Exploring the Capabilities of Prompted Large Language Models in Educational and Assessment Applications
  4. 2025Benchmarking Critical Questions Generation: A Challenging Reasoning Task for Large Language Models
  5. 2024Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects
  6. 2026Cognitively Diverse Multiple-Choice Question Generation: A Hybrid Multi-Agent Framework with Large Language Models
  7. 2023Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation
  8. 2026Hallucination-Free Automatic Question & Answer Generation for Intuitive Learning