• Feng et al 2021 - A Survey of Data Augmentation Approaches for NLP

• Shen et al 2020 - A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation

• Jia & Liang 2016 - Data Recombination for Neural Semantic Parsing
• Andreas 2019 - Good-Enough Compositional Data Augmentation GECA method
• Guo et al 2020 - Sequence-Level Mixed Sample Data Augmentation

Aka synthetic data generation. For evaluation, see Evaluation with Large Language Models.

• Overviews
  • Tan et al 2024 - Large Language Models for Data Annotation and Synthesis: A Survey
  • Long et al 2024 - On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey Really great survey, lots of practical advice
  • 2024 - Hands-On Tutorial: Labeling with LLM and Human-in-the-Loop slides from here
• Wang et al 2021 - Want To Reduce Labeling Cost? GPT-3 Can Help
• Wang et al 2022 - PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks
• Ding et al 2022 - Is GPT-3 a Good Data Annotator?
• Meng et al 2022 - Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning
• Guo et al 2023 - The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Text
• Zhang & Pavlick 2024 - Does Training on Synthetic Data Make Models Less Robust?
• Patel et al 2025 - How to Get Your LLM to Generate Challenging Problems for Evaluation

• Crowdsourcing
• Dataset Creation
• ML - Data Augmentation
• Semantic Parsing - Data Augmentation