mlsanityData to 7 Oct 2026

QOG:Question and Options Generation based on Language Model

arXiv 2406.12381 ↗First posted 18 Jun 2024cs.CL cs.AI cs.LGcited by 1

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Abstract (arXiv)

Question-Options Generation (QOG) is a task that involves generating a set of question-options pairs given context. This task has various applications, including fine-tuning large models, information retrieval, and automated multiple-choice question generation for education. In this paper, we develop QOG models using three different methods based on fine-tuning sequence-to-sequence language models (LMs). Experiments demonstrate that the end-to-end QOG model is computationally efficient and stable during both training and inference, outperforming other methods. Furthermore, our analysis indicates that our QOG models are competitive on the QOG task compared to the large language model Llama 3-8B.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 27 of its 35 references that are in the map. First posted on arXiv 18 Jun 2024.

Builds on

27 of 35 references.

References · 27

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15 more
  1. 2020Training Question Answering Models From Synthetic Datacited by 95
  2. 2021Improving Question Answering Model Robustness with Synthetic Adversarial Data Generationcited by 77
  3. 2019Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answeringcited by 63
  4. 2020Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answeringcited by 41
  5. 2019XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answeringcited by 35
  6. 2022Generative Language Models for Paragraph-Level Question Generationcited by 28
  7. 2023Assessing the Quality of Multiple-Choice Questions Using GPT-4 and Rule-Based Methodscited by 23
  8. 2019An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answeringcited by 22
  9. 2021Asking It All: Generating Contextualized Questions for any Semantic Rolecited by 22
  10. 2023PaniniQA: Enhancing Patient Education Through Interactive Question Answeringcited by 21
  11. 2020Local Self-Attention over Long Text for Efficient Document Retrievalcited by 18
  12. 2023An Empirical Comparison of LM-based Question and Answer Generation Methodscited by 15
  13. 2020Accelerating Real-Time Question Answering via Question Generationcited by 11
  14. 2019EDUQA: Educational Domain Question Answering System using Conceptual Network Mappingcited by 3
  15. 2023QASnowball: An Iterative Bootstrapping Framework for High-Quality Question-Answering Data Generationcited by 1

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Citing papers per year: 2025: 1.

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  1. 2025Grounding Synthetic Data Evaluations of Language Models in Unsupervised Document Corporacited by 2