Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance

• URL: http://arxiv.org/abs/2406.03628v2
• Date: Mon, 06 Jan 2025 15:37:48 GMT
• Title: Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance
• Authors: Ryumei Nakada, Yichen Xu, Lexin Li, Linjun Zhang,
• Abstract summary: Imbalanced classification and spurious correlation are common challenges in data science and machine learning.<n>Recent advances have proposed leveraging the flexibility and generative capabilities of large language models to generate synthetic samples.<n>This article develops novel theoretical foundations to systematically study the roles of synthetic samples in addressing imbalanced classification and spurious correlation.
• Score: 16.047084318753377
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Imbalanced classification and spurious correlation are common challenges in data science and machine learning. Both issues are linked to data imbalance, with certain groups of data samples significantly underrepresented, which in turn would compromise the accuracy, robustness and generalizability of the learned models. Recent advances have proposed leveraging the flexibility and generative capabilities of large language models (LLMs), typically built on transformer architectures, to generate synthetic samples and to augment the observed data. In the context of imbalanced data, LLMs are used to oversample underrepresented groups and have shown promising improvements. However, there is a clear lack of theoretical understanding of such synthetic data approaches. In this article, we develop novel theoretical foundations to systematically study the roles of synthetic samples in addressing imbalanced classification and spurious correlation. Specifically, we first explicitly quantify the benefits of synthetic oversampling. Next, we analyze the scaling dynamics in synthetic data augmentation, and derive the corresponding scaling law. Finally, we demonstrate the capacity of transformer models to generate high-quality synthetic samples. We further conduct extensive numerical experiments to validate the efficacy of the LLM-based synthetic oversampling and augmentation.

Related papers

• Scaling Laws of Synthetic Data for Language Models [132.67350443447611]
  We introduce SynthLLM, a scalable framework that transforms pre-training corpora into diverse, high-quality synthetic datasets. Our approach achieves this by automatically extracting and recombining high-level concepts across multiple documents using a graph algorithm.
  arXiv  Detail & Related papers  (2025-03-25T11:07:12Z)
• A Theoretical Perspective: How to Prevent Model Collapse in Self-consuming Training Loops [55.07063067759609]
  High-quality data is essential for training large generative models, yet the vast reservoir of real data available online has become nearly depleted. Models increasingly generate their own data for further training, forming Self-consuming Training Loops (STLs) Some models degrade or even collapse, while others successfully avoid these failures, leaving a significant gap in theoretical understanding.
  arXiv  Detail & Related papers  (2025-02-26T06:18:13Z)
• On the Diversity of Synthetic Data and its Impact on Training Large Language Models [34.00031258223175]
  Large Language Models (LLMs) have accentuated the need for diverse, high-quality pre-training data. Synthetic data emerges as a viable solution to the challenges of data scarcity and inaccessibility. We study the downstream effects of synthetic data diversity during both the pre-training and fine-tuning stages.
  arXiv  Detail & Related papers  (2024-10-19T22:14:07Z)
• Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models [89.88010750772413]
  Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs) Our work delves into these specific flaws associated with question-answer (Q-A) pairs, a prevalent type of synthetic data, and presents a method based on unlearning techniques to mitigate these flaws. Our work has yielded key insights into the effective use of synthetic data, aiming to promote more robust and efficient LLM training.
  arXiv  Detail & Related papers  (2024-06-18T08:38:59Z)
• Synthetic Information towards Maximum Posterior Ratio for deep learning on Imbalanced Data [1.7495515703051119]
  We propose a technique for data balancing by generating synthetic data for the minority class. Our method prioritizes balancing the informative regions by identifying high entropy samples. Our experimental results on forty-one datasets demonstrate the superior performance of our technique.
  arXiv  Detail & Related papers  (2024-01-05T01:08:26Z)
• Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark [56.8042116967334]
  Synthetic data serves as an alternative in training machine learning models. ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task. This paper explores the potential of integrating data-centric AI techniques to guide the synthetic data generation process.
  arXiv  Detail & Related papers  (2023-10-25T20:32:02Z)
• Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models [69.76066070227452]
  *Data Synthesis* is a promising way to train a small model with very little labeled data. We propose *Synthesis Step by Step* (**S3**), a data synthesis framework that shrinks this distribution gap. Our approach improves the performance of a small model by reducing the gap between the synthetic dataset and the real data.
  arXiv  Detail & Related papers  (2023-10-20T17:14:25Z)
• Does Synthetic Data Make Large Language Models More Efficient? [0.0]
  This paper explores the nuances of synthetic data generation in NLP. We highlight its advantages, including data augmentation potential and the introduction of structured variety. We demonstrate the impact of template-based synthetic data on the performance of modern transformer models.
  arXiv  Detail & Related papers  (2023-10-11T19:16:09Z)
• Tackling Diverse Minorities in Imbalanced Classification [80.78227787608714]
  Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. We propose generating synthetic samples iteratively by mixing data samples from both minority and majority classes. We demonstrate the effectiveness of our proposed framework through extensive experiments conducted on seven publicly available benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-28T18:48:34Z)
• Utility Theory of Synthetic Data Generation [12.511220449652384]
  This paper bridges the practice-theory gap by establishing relevant utility theory in a statistical learning framework. It considers two utility metrics: generalization and ranking of models trained on synthetic data.
  arXiv  Detail & Related papers  (2023-05-17T07:49:16Z)
• Synthetic data, real errors: how (not) to publish and use synthetic data [86.65594304109567]
  We show how the generative process affects the downstream ML task. We introduce Deep Generative Ensemble (DGE) to approximate the posterior distribution over the generative process model parameters.
  arXiv  Detail & Related papers  (2023-05-16T07:30:29Z)
• CAFE: Learning to Condense Dataset by Aligning Features [72.99394941348757]
  We propose a novel scheme to Condense dataset by Aligning FEatures (CAFE) At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art.
  arXiv  Detail & Related papers  (2022-03-03T05:58:49Z)
• A Scaling Law for Synthetic-to-Real Transfer: A Measure of Pre-Training [52.93808218720784]
  Synthetic-to-real transfer learning is a framework in which we pre-train models with synthetically generated images and ground-truth annotations for real tasks. Although synthetic images overcome the data scarcity issue, it remains unclear how the fine-tuning performance scales with pre-trained models. We observe a simple and general scaling law that consistently describes learning curves in various tasks, models, and complexities of synthesized pre-training data.
  arXiv  Detail & Related papers  (2021-08-25T02:29:28Z)
• Towards Synthetic Multivariate Time Series Generation for Flare Forecasting [5.098461305284216]
  One of the limiting factors in training data-driven, rare-event prediction algorithms is the scarcity of the events of interest. In this study, we explore the usefulness of the conditional generative adversarial network (CGAN) as a means to perform data-informed oversampling.
  arXiv  Detail & Related papers  (2021-05-16T22:23:23Z)
• Foundations of Bayesian Learning from Synthetic Data [1.6249267147413522]
  We use a Bayesian paradigm to characterise the updating of model parameters when learning on synthetic data. Recent results from general Bayesian updating support a novel and robust approach to synthetic-learning founded on decision theory.
  arXiv  Detail & Related papers  (2020-11-16T21:49:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.