| Challenge: | Existing data augmentation methods focus on increasing sample numbers while neglecting sample distribution diversity, which can lead to model overfitting. |
| Approach: | They propose a data augmentation framework that focuses on sample distribution diversity and trains a large language model as a diverse paraphraser. |
| Outcome: | The proposed framework achieves an average performance gain of 10.52% surpassing the runner-up baseline with more than three percentage points. |
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Empowering Large Language Models for Textual Data Augmentation (2024.findings-acl)
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| Challenge: | True. True. False |
| Approach: | False slants are proposed to generate a large pool of augmentation instructions and select the most suitable task-informed instructions. |
| Outcome: | False omissions: the proposed approach consistently generates augmented data with better quality compared to non-LLM and LLM-based data augmentation methods. |
Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges (2024.findings-acl)
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Bosheng Ding, Chengwei Qin, Ruochen Zhao, Tianze Luo, Xinze Li, Guizhen Chen, Wenhan Xia, Junjie Hu, Anh Tuan Luu, Shafiq Joty
| Challenge: | Data augmentation (DA) is a key technique for enhancing model performance by diversifying training examples without the need for additional data collection. |
| Approach: | They examine various strategies that utilize LLMs for data augmentation, including a novel exploration of learning paradigms where LLM-generated data is used for diverse forms of further training. |
| Outcome: | The proposed approach addresses the primary open challenges faced by LLMs in the field of large language models and aims to serve as a comprehensive guide for researchers and practitioners. |
LLM-powered Data Augmentation for Enhanced Cross-lingual Performance (2023.emnlp-main)
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| Challenge: | Existing training data for multilingual commonsense reasoning datasets is limited. |
| Approach: | They propose to use large language models for data augmentation in multilingual datasets . they use Dolly-v2, StableVicuna, ChatGPT, and GPT-4 to augment three datasets. |
| Outcome: | The proposed model outperforms larger general-purpose, zero-shot models when training in smaller models. |
Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models (2021.emnlp-main)
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| Challenge: | Recent studies have shown that powerful pre-trained language models can be fooled by small perturbations or intentional attacks. |
| Approach: | They propose a framework for fine-tuning PLMs using a masked language model and Gaussian noise to augment semantically relevant examples with sufficient diversity. |
| Outcome: | The proposed framework improves the robustness of pre-trained language models and alleviates performance degradation under adversarial attacks. |
Enhancing LLM Knowledge Learning through Generalization (2025.findings-emnlp)
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| Challenge: | Continued pre-training on paraphrased data has shown empirical promise for enhancing knowledge acquisition, but this approach is costly and unreliable as it relies on external models or manual effort for rewriting. |
| Approach: | They propose formatting-based data augmentation which diversifies documents conveying the same knowledge by altering document formats rather than their content. |
| Outcome: | The proposed methods improve generalization to diverse paraphrased contexts and enhance pre-training and instruction tuning. |
CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP (2024.findings-naacl)
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| Challenge: | a low-resource dataset is limited in training data, so generating task-specific data is challenging. |
| Approach: | They propose a data augmentation technique that prompts off-the-shelf instruction-following Large Language Models to generate augmentations. |
| Outcome: | The proposed technique outperforms baselines on 11 datasets spanning 3 tasks and 3 low-resource settings. |
Evaluating the Effectiveness and Scalability of LLM-Based Data Augmentation for Retrieval (2025.emnlp-main)
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| Challenge: | Existing research does not explore key factors such as optimal augmentation scale and the necessity of using large augmentation models. |
| Approach: | They propose to use LLMs to augment compact dual-encoder models to improve retrieval performance. |
| Outcome: | The proposed approach improves retrieval performance but its benefits diminish beyond a certain scale even with diverse augmentation strategies. |
Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation (2024.acl-long)
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| Challenge: | generative large language models (LLMs) have found their application in data augmentation tasks, where small numbers of text samples are paraphrased and then used to fine-tune downstream models. |
| Approach: | They propose to use taboo words, hints by previous outlier solutions, and chaining on previous outliest solutions to augment text datasets as part of instructions to LLMs augmenting text dataset. |
| Outcome: | The proposed methods increase diversity of generated texts, but performance is highest with hints. |
DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers (2025.acl-long)
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| Challenge: | Large language models (LLMs) have shown strong effectiveness and robustness when fine-tuned as dense retrievers. |
| Approach: | They propose a training framework that leverages pruned LLMs to train smaller generalizable dense retrievers. |
| Outcome: | The proposed training framework offers better multilingual and long-context capabilities than traditional encoder-based retrievers and achieves strong performance across multiple tasks and languages. |
Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment (2024.lrec-main)
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| Challenge: | Large language models (LLMs) can reveal toxic or offensive content inadvertently or intentionally. |
| Approach: | They propose to control the diversity of both sides according to the number of samples for fine-tuning, which can directly reflect their impact. |
| Outcome: | The proposed approach improves the performance of large language models after fine-tuning. |