arXiv subjects

Assaad Zeghina

Publications and source records attributed to Assaad Zeghina.

2 recordsLinked to original sources

arXiv2025

Orthogonal Procrustes problem preserves correlations in synthetic data

Synthetic data generation is increasingly used in applications involving privacy preservation, data sharing, and data scarcity. In many situations, preserving the dependence structure of the original data is of central interest. In this work, we propose a lightweight postprocessing methodology for synthetic tabular data based on the Orthogonal Procrustes problem. Starting from an already generated synthetic dataset, our approach constructs the closest dataset that restores the Pearson correlation structure of the original data. On the theoretical side, we show that preserving Pearson correlation is equivalent to the action of linear orthogonal maps in the centered-data subspace, and then deploy the Orthogonal Procrustes problem. However, in order for this to hold, we first establish a result ensuring that applying the Orthogonal Procrustes step remains in the aforementioned subspace under suitable assumptions. Applications to several datasets and synthetic data generators illustrate the effectiveness of the proposed approach. In particular, the numerical experiments indicate that the correlation structure can be restored while largely preserving the individual feature distributions, the geometry of the data, and the performance of downstream classification tasks.

stat.ME↗

arXiv2025

LLM-based Semantic Augmentation for Harmful Content Detection

Recent advances in large language models (LLMs) have demonstrated strong performance on simple text classification tasks, frequently under zero-shot settings. However, their efficacy declines when tackling complex social media challenges such as propaganda detection, hateful meme classification, and toxicity identification. Much of the existing work has focused on using LLMs to generate synthetic training data, overlooking the potential of LLM-based text preprocessing and semantic augmentation. In this paper, we introduce an approach that prompts LLMs to clean noisy text and provide context-rich explanations, thereby enhancing training sets without substantial increases in data volume. We systematically evaluate on the SemEval 2024 multi-label Persuasive Meme dataset and further validate on the Google Jigsaw toxic comments and Facebook hateful memes datasets to assess generalizability. Our results reveal that zero-shot LLM classification underperforms on these high-context tasks compared to supervised models. In contrast, integrating LLM-based semantic augmentation yields performance on par with approaches that rely on human-annotated data, at a fraction of the cost. These findings underscore the importance of strategically incorporating LLMs into machine learning (ML) pipeline for social media classification tasks, offering broad implications for combating harmful content online.

cs.CL↗