mlsanityData to 14 Oct 2026

An Empirical Comparison of LM-based Question and Answer Generation Methods

arXiv 2305.17002 ↗First posted 26 May 2023cs.CLcited by 15

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Abstract (arXiv)

Question and answer generation (QAG) consists of generating a set of question-answer pairs given a context (e.g. a paragraph). This task has a variety of applications, such as data augmentation for question answering (QA) models, information retrieval and education. In this paper, we establish baselines with three different QAG methodologies that leverage sequence-to-sequence language model (LM) fine-tuning. Experiments show that an end-to-end QAG model, which is computationally light at both training and inference times, is generally robust and outperforms other more convoluted approaches. However, there are differences depending on the underlying generative LM. Finally, our analysis shows that QA models fine-tuned solely on generated question-answer pairs can be competitive when compared to supervised QA models trained on human-labeled data.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 24 of its 34 references that are in the map. First posted on arXiv 26 May 2023.

Builds on

24 of 34 references.

References · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  4. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  5. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  6. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  7. 2021Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLPcited by 337
  8. 2021PAQ: 65 Million Probably-Asked Questions and What You Can Do With Themcited by 200
  9. 2017Learning to Ask: Neural Question Generation for Reading Comprehensioncited by 186
  10. 2019Synthetic QA Corpora Generation with Roundtrip Consistencycited by 143
  11. 2020Unsupervised Question Decomposition for Question Answeringcited by 118
  12. 2020The Effect of Natural Distribution Shift on Question Answering Modelscited by 104
12 more
  1. 2020Training Question Answering Models From Synthetic Datacited by 95
  2. 2021Improving Question Answering Model Robustness with Synthetic Adversarial Data Generationcited by 79
  3. 2017Neural Question Generation from Text: A Preliminary Studycited by 78
  4. 2020End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systemscited by 65
  5. 2019Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answeringcited by 63
  6. 2019Unsupervised Question Answering by Cloze Translationcited by 59
  7. 2018Harvesting Paragraph-Level Question-Answer Pairs from Wikipediacited by 49
  8. 2020Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEscited by 31
  9. 2021MixQG: Neural Question Generation with Mixed Answer Typescited by 28
  10. 2022Generative Language Models for Paragraph-Level Question Generationcited by 28
  11. 2021Asking It All: Generating Contextualized Questions for any Semantic Rolecited by 22
  12. 2022RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Questioncited by 17

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  1. 2024Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMscited by 63
  2. 2024Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledgecited by 50
  3. 2024Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptationcited by 27
  4. 2024TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learningcited by 22
  5. 2023A Practical Toolkit for Multilingual Question and Answer Generationcited by 10
  6. 2025Do LLMs Understand Your Translations? Evaluating Paragraph-level MT with Question Answeringcited by 10
  7. 2024An Empirical Comparison of Generative Approaches for Product Attribute-Value Identificationcited by 7
  8. 2024QAEA-DR: A Unified Text Augmentation Framework for Dense Retrievalcited by 4
  9. 2024KBAlign: Efficient Self Adaptation on Specific Knowledge Basescited by 3
  10. 2024Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Modelscited by 2
  11. 2025Grounding Synthetic Data Evaluations of Language Models in Unsupervised Document Corporacited by 2
  12. 2026DeepEra: A Deep Evidence Reranking Agent for Scientific Retrieval-Augmented Generated Question Answeringcited by 2
3 more
  1. 2024QOG:Question and Options Generation based on Language Modelcited by 1
  2. 2025Domain-Specific Data Generation Framework for RAG Adaptationcited by 1
  3. 2024How to Engage Your Readers? Generating Guiding Questions to Promote Active Readingcited by 0