Data-Constrained Synthesis of Training Data for De-Identification

• URL: http://arxiv.org/abs/2502.14677v2
• Date: Fri, 21 Feb 2025 16:58:44 GMT
• Title: Data-Constrained Synthesis of Training Data for De-Identification
• Authors: Thomas Vakili, Aron Henriksson, Hercules Dalianis,
• Abstract summary: We domain-adapt large language models (LLMs) to the clinical domain.<n>We generate synthetic clinical texts that are machine-annotated with tags for personally identifiable information.<n>The synthetic corpora are then used to train synthetic NER models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Many sensitive domains -- such as the clinical domain -- lack widely available datasets due to privacy risks. The increasing generative capabilities of large language models (LLMs) have made synthetic datasets a viable path forward. In this study, we domain-adapt LLMs to the clinical domain and generate synthetic clinical texts that are machine-annotated with tags for personally identifiable information using capable encoder-based NER models. The synthetic corpora are then used to train synthetic NER models. The results show that training NER models using synthetic corpora incurs only a small drop in predictive performance. The limits of this process are investigated in a systematic ablation study -- using both Swedish and Spanish data. Our analysis shows that smaller datasets can be sufficient for domain-adapting LLMs for data synthesis. Instead, the effectiveness of this process is almost entirely contingent on the performance of the machine-annotating NER models trained using the original data.

Related papers

• Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data [6.318463500874778]
  We propose a prototype-guided diffusion model to generate high-fidelity synthetic pathology data at scale. Our approach ensures biologically and diagnostically meaningful variations in the generated data. We demonstrate that self-supervised features trained on our synthetic dataset achieve competitive performance despite using 60x-760x less data than models trained on large real-world datasets.
  arXiv  Detail & Related papers  (2025-04-15T21:17:39Z)
• 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)
• Creating Artificial Students that Never Existed: Leveraging Large Language Models and CTGANs for Synthetic Data Generation [2.4374097382908477]
  We investigate whether synthetic data can be leveraged to create artificial students for serving learning analytics models.<n>Our results demonstrate the strong potential of these methods to produce high-quality synthetic datasets that resemble real students data.
  arXiv  Detail & Related papers  (2025-01-03T12:52:51Z)
• Efficacy of Synthetic Data as a Benchmark [3.2968976262860408]
  We investigate the effectiveness of generating synthetic data through large language models (LLMs) Our experiments show that while synthetic data can effectively capture performance of various methods for simpler tasks, it falls short for more complex tasks like named entity recognition. We propose a new metric called the bias factor, which evaluates the biases introduced when the same LLM is used to both generate benchmarking data and to perform the tasks.
  arXiv  Detail & Related papers  (2024-09-18T13:20:23Z)
• 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)
• 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)
• Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations [21.583825474908334]
  We study how the performance of models trained on synthetic data may vary with the subjectivity of classification. Our results indicate that subjectivity, at both the task level and instance level, is negatively associated with the performance of the model trained on synthetic data.
  arXiv  Detail & Related papers  (2023-10-11T19:51:13Z)
• 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)
• TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series [61.436361263605114]
  Time series data are often scarce or highly sensitive, which precludes the sharing of data between researchers and industrial organizations. We introduce Time Series Generative Modeling (TSGM), an open-source framework for the generative modeling of synthetic time series.
  arXiv  Detail & Related papers  (2023-05-19T10:11:21Z)
• 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)

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.