mlsanityData to 7 Oct 2026

Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges

arXiv 2309.12426 ↗First posted 21 Sep 2023cs.CL cs.AIcited by 9

Not on the map. Why?

Abstract (arXiv)

Large Language Models (LLMs) have demonstrated impressive zero shot performance on a wide range of NLP tasks, demonstrating the ability to reason and apply commonsense. A relevant application is to use them for creating high quality synthetic datasets for downstream tasks. In this work, we probe whether GPT-4 can be used to augment existing extractive reading comprehension datasets. Automating data annotation processes has the potential to save large amounts of time, money and effort that goes into manually labelling datasets. In this paper, we evaluate the performance of GPT-4 as a replacement for human annotators for low resource reading comprehension tasks, by comparing performance after fine tuning, and the cost associated with annotation. This work serves to be the first analysis of LLMs as synthetic data augmenters for QA systems, highlighting the unique opportunities and challenges. Additionally, we release augmented versions of low resource datasets, that will allow the research community to create further benchmarks for evaluation of generated datasets.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 31 of its 47 references that are in the map. First posted on arXiv 21 Sep 2023.

Builds on

31 of 47 references.

References · 31

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,538
  4. 2019CTRL: A Conditional Transformer Language Model for Controllable Generationcited by 754
  5. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  6. 2021All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Textcited by 287
  7. 2022Generating Training Data with Language Models: Towards Zero-Shot Language Understandingcited by 199
  8. 2021Generating Datasets with Pretrained Language Modelscited by 192
  9. 2021GPT3Mix: Leveraging Large-scale Language Models for Text Augmentationcited by 185
  10. 2019Not Enough Data? Deep Learning to the Rescue!cited by 174
  11. 2020Data Augmentation using Pre-trained Transformer Modelscited by 168
  12. 2019Synthetic QA Corpora Generation with Roundtrip Consistencycited by 143
19 more
  1. 2018emrQA: A Large Corpus for Question Answering on Electronic Medical Recordscited by 134
  2. 2021A Recipe For Arbitrary Text Style Transfer with Large Language Modelscited by 107
  3. 2021Improving Question Answering Model Robustness with Synthetic Adversarial Data Generationcited by 77
  4. 2020End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systemscited by 65
  5. 2022PromDA: Prompt-based Data Augmentation for Low-Resource NLU Taskscited by 51
  6. 2018Harvesting Paragraph-Level Question-Answer Pairs from Wikipediacited by 49
  7. 2020DARE: Data Augmented Relation Extraction with GPT-2cited by 43
  8. 2021Generate, Annotate, and Learn: NLP with Synthetic Textcited by 42
  9. 2020Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answeringcited by 41
  10. 2019Span Selection Pre-training for Question Answeringcited by 36
  11. 2020BIOMRC: A Dataset for Biomedical Machine Reading Comprehensioncited by 35
  12. 2021CRASS: A Novel Data Set and Benchmark to Test Counterfactual Reasoning of Large Language Modelscited by 33
  13. 2020PolicyQA: A Reading Comprehension Dataset for Privacy Policiescited by 31
  14. 2019The TechQA Datasetcited by 29
  15. 2017Semi-Supervised QA with Generative Domain-Adaptive Netscited by 27
  16. 2020Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructionscited by 25
  17. 2022CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generationcited by 23
  18. 2018Simple and Effective Semi-Supervised Question Answeringcited by 22
  19. 2022Combining Data Generation and Active Learning for Low-Resource Question Answeringcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 1.

9 papers.

  1. 2024A Survey on Data Augmentation in Large Model Eracited by 31
  2. 2024LlamaDuo: LLMOps Pipeline for Seamless Migration from Service LLMs to Small-Scale Local LLMscited by 8
  3. 2025What Has Been Lost with Synthetic Evaluation?cited by 8
  4. 2024Tool Calling: Enhancing Medication Consultation via Retrieval-Augmented Large Language Modelscited by 6
  5. 2025Position: LLMs Can be Good Tutors in English Educationcited by 6
  6. 2025Measuring Diversity in Synthetic Datasetscited by 5
  7. 2025Enhancing Arabic Automated Essay Scoring with Synthetic Data and Error Injectioncited by 5
  8. 2025Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Modelscited by 3
  9. 2026Emotion is Not Just a Label: Latent Emotional Factors in LLM Processingcited by 1