# PromptAug: Fine-grained Conflict Classification Using Data Augmentation

arXiv: https://arxiv.org/abs/2506.22491 · HTML: https://mlsanity.com/paper/2506.22491/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 37 of its 98 references that are in the map.
- First posted on arXiv 24 Jun 2025.

## Builds on (most cited first, 30 of 37)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Cross-Task Generalization via Natural Language Crowdsourcing Instructions](https://mlsanity.com/paper/2104.08773/) (2021)
- [Texygen: A Benchmarking Platform for Text Generation Models](https://mlsanity.com/paper/1802.01886/) (2018)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://mlsanity.com/paper/2111.07997/) (2021)
- [On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://mlsanity.com/paper/2406.15126/) (2024)
- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70B](https://mlsanity.com/paper/2310.20624/) (2023)
- [GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation](https://mlsanity.com/paper/2104.08826/) (2021)
- [Not Enough Data? Deep Learning to the Rescue!](https://mlsanity.com/paper/1911.03118/) (2019)
- [Directions in Abusive Language Training Data: Garbage In, Garbage Out](https://mlsanity.com/paper/2004.01670/) (2020)
- [AugGPT: Leveraging ChatGPT for Text Data Augmentation](https://mlsanity.com/paper/2302.13007/) (2023)
- [Conditional BERT Contextual Augmentation](https://mlsanity.com/paper/1812.06705/) (2018)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [A Large Self-Annotated Corpus for Sarcasm](https://mlsanity.com/paper/1704.05579/) (2017)
- [Probing LLMs for hate speech detection: strengths and vulnerabilities](https://mlsanity.com/paper/2310.12860/) (2023)
- [LLM-powered Data Augmentation for Enhanced Cross-lingual Performance](https://mlsanity.com/paper/2305.14288/) (2023)
- [Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge](https://mlsanity.com/paper/2403.01432/) (2024)
- [Generative Data Augmentation for Commonsense Reasoning](https://mlsanity.com/paper/2004.11546/) (2020)
- [Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue](https://mlsanity.com/paper/1709.05404/) (2017)
- [PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation](https://mlsanity.com/paper/2310.14192/) (2023)
- [Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search](https://mlsanity.com/paper/2405.03480/) (2024)

## Abstract (arXiv)

Given the rise of conflicts on social media, effective classification models to detect harmful behaviours are essential. Following the garbage-in-garbage-out maxim, machine learning performance depends heavily on training data quality. However, high-quality labelled data, especially for nuanced tasks like identifying conflict behaviours, is limited, expensive, and difficult to obtain. Additionally, as social media platforms increasingly restrict access to research data, text data augmentation is gaining attention as an alternative to generate training data. Augmenting conflict-related data poses unique challenges due to Large Language Model (LLM) guardrails that prevent generation of offensive content. This paper introduces PromptAug, an innovative LLM-based data augmentation method. PromptAug achieves statistically significant improvements of 2% in both accuracy and F1-score on conflict and emotion datasets. To thoroughly evaluate PromptAug against other data augmentation methods we conduct a robust evaluation using extreme data scarcity scenarios, quantitative diversity analysis and a qualitative thematic analysis. The thematic analysis identifies four problematic patterns in augmented text: Linguistic Fluidity, Humour Ambiguity, Augmented Content Ambiguity, and Augmented Content Misinterpretation. Overall, this work presents PromptAug as an effective method for augmenting data in sensitive tasks like conflict detection, offering a unique, interdisciplinary evaluation grounded in both natural language processing and social science methodology.