mlsanityData to 14 Oct 2026

Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generation

arXiv 2404.00361 ↗First posted 30 Mar 2024cs.CLcited by 4

Not on the map. Why?

Abstract (arXiv)

Data augmentation (DA) is crucial to mitigate model training instability and over-fitting problems in low-resource open-domain dialogue generation. However, traditional DA methods often neglect semantic data diversity, restricting the overall quality. Recently, large language models (LLM) have been used for DA to generate diversified dialogues. However, they have limited controllability and tend to generate dialogues with a distribution shift compared to the seed dialogues. To maximize the augmentation diversity and address the controllability problem, we propose Summary-based Dialogue Augmentation with LLM (SDA). Our approach enhances the controllability of LLM by using dialogue summaries as a planning tool. Based on summaries, SDA can generate high-quality and diverse dialogue data even with a small seed dataset. To evaluate the efficacy of data augmentation methods for open-domain dialogue, we designed a clustering-based metric to characterize the semantic diversity of the augmented dialogue data. The experimental results show that SDA can augment high-quality and semantically diverse dialogues given a small seed dataset and an LLM, and the augmented data can boost the performance of open-domain dialogue models.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 36 of its 44 references that are in the map. First posted on arXiv 30 Mar 2024.

Builds on

36 of 44 references.

References · 36

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  5. 2020Scaling Laws for Neural Language Modelscited by 6,586
  6. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  7. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
  8. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,125
  9. 2015A Diversity-Promoting Objective Function for Neural Conversation Modelscited by 1,077
  10. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,060
  11. 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
  12. 2018Personalizing Dialogue Agents: I have a dog, do you have pets too?cited by 828
24 more
  1. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 807
  2. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 788
  3. 2017DailyDialog: A Manually Labelled Multi-turn Dialogue Datasetcited by 652
  4. 2021Learning To Retrieve Prompts for In-Context Learningcited by 648
  5. 2020Recipes for building an open-domain chatbotcited by 593
  6. 2018Wizard of Wikipedia: Knowledge-Powered Conversational agentscited by 530
  7. 2021A Survey of Data Augmentation Approaches for NLPcited by 409
  8. 2022UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Modelscited by 231
  9. 2019Not Enough Data? Deep Learning to the Rescue!cited by 175
  10. 2020SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustnesscited by 96
  11. 2021AEDA: An Easier Data Augmentation Technique for Text Classificationcited by 67
  12. 2021Few-Shot Bot: Prompt-Based Learning for Dialogue Systemscited by 64
  13. 2022PromDA: Prompt-based Data Augmentation for Low-Resource NLU Taskscited by 51
  14. 2021Few-Shot Semantic Parsing with Language Models Trained On Codecited by 50
  15. 2023ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extractioncited by 38
  16. 2021Few-shot Intent Classification and Slot Filling with Retrieved Examplescited by 35
  17. 2021Controllable Semantic Parsing via Retrieval Augmentationcited by 35
  18. 2020Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweightcited by 34
  19. 2022Weakly Supervised Data Augmentation Through Prompting for Dialogue Understandingcited by 32
  20. 2022CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsingcited by 21
  21. 2023Exploring Zero and Few-shot Techniques for Intent Classificationcited by 21
  22. 2020Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Datacited by 20
  23. 2022LAD: Language Models as Data for Zero-Shot Dialogcited by 18
  24. 2022Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialoguescited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2024Learning to Refuse: Towards Mitigating Privacy Risks in LLMscited by 23
  2. 2024A Survey of Data Synthesis Approachescited by 9
  3. 2025Boosting Large Language Models for Mental Manipulation Detection via Data Augmentation and Distillationcited by 2
  4. 2026SafeTalkCoach: Diversity-Driven Multi-Agent Simulation for Parent-Teen Health Conversationscited by 0