Introducing the Synthetic Data Generator - Build Datasets with Natural Language

Achieving Precise Text-To-Cypher Via Grounded Knowledge Graph Data Generation

arXiv:2606. 14325v1 Announce Type: cross Abstract: Property Graphs are rapidly being adopted as database frameworks for representing heterogeneous data sources.

Cosmopedia: how to create large-scale synthetic data for pre-training Large Language Models

How Can We Synthesize High-Quality Pretraining Data? A Systematic Study of Prompt Design, Generator Model, and Source Data

arXiv:2604. 13977v2 Announce Type: replace-cross Abstract: Synthetic data is a standard component in training large language models, yet systematic comparisons across design dimensions, including rephrasing strategy, generator model, and source data, remain absent.

A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data

arXiv:2601. 17717v3 Announce Type: replace Abstract: Large Language Models (LLMs) have emerged as powerful tools for generating data across various modalities.

Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation

arXiv:2604. 07486v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have emerged as a powerful tool for synthetic data generation.

RingSQL: Schema-Independent Synthetic Data Generation for Text-to-SQL Reinforcement Learning

arXiv:2601. 05451v2 Announce Type: replace Abstract: Recent advances in text-to-SQL have been driven by larger models, better datasets, and new training methods like RLVR.

CodeAlchemy: Synthetic Code Rewriting at Scale

arXiv:2606. 10087v1 Announce Type: cross Abstract: Pre-training on raw code teaches syntax but provides sparse signal for diverse real-world task formats.

Synthics: Synthetic Physics-like Datasets for Machine Learning

arXiv:2606. 06724v1 Announce Type: new Abstract: Representative data is fundamental in machine learning, as limited data hinders generalisation.

Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets

arXiv:2605. 17758v2 Announce Type: replace Abstract: Synthetic data is widely used in healthcare to create datasets that preserve statistical properties of real data without exposing sensitive patient information.

Disjoint Generation of Synthetic Data

arXiv:2507. 19700v2 Announce Type: replace Abstract: We propose a new framework for generating tabular synthetic datasets via disjoint generative models.

Hierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Framework

arXiv:2605. 28198v2 Announce Type: replace Abstract: Existing approaches for synthetic tabular data generation are based on either purely generative models or LLMs, both of which struggle with data heterogeneity, logical consistency, rare-event coverage, and robustness in low-data regimes.

Learning from flowsheets: A generative transformer model for autocompletion of flowsheets

arXiv:2208. 00859v2 Announce Type: replace Abstract: We propose a novel method enabling autocompletion of chemical flowsheets.