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This research examines whether LLMs can generate synthetic consumer data for projective techniques, comparing human and LLM responses on city tourism perceptions and finding substantial overlap but differences in style and diversity.
This paper introduces Autodata, a method that uses an agentic 'data scientist' AI to automate the creation of high-quality synthetic datasets through iterative generation, verification, and refinement, specifically optimized for reinforcement learning (GRPO) to improve reasoning in language models.
TDGT is a web-based toolkit for synthetic tabular data generation that introduces the Adaptive Bayesian Mixture Synthesizer (ABMS) algorithm and a hybrid VAE-ABMS model, with GPU acceleration and comprehensive fidelity assessment.
Autodata introduces an agentic data scientist that iteratively generates and refines synthetic training data, with meta-optimization to further improve data quality, achieving better results on computer science and legal reasoning tasks.
Meta's new paper 'Autodata' introduces an agentic data scientist that generates and meta-optimizes synthetic training data, significantly outperforming standard methods and enabling a small 4B model to beat a 397B baseline in legal tasks.
Meta releases Autodata, an agentic data scientist that generates high-quality synthetic data by iteratively refining task difficulty using multiple LLMs, with output used for GRPO training.
This paper presents a synthetic data generation method for fine-tuning small LLMs to convert natural language to Cypher queries for property graphs, achieving competitive performance with large proprietary models while enabling local deployment and data sovereignty.
ReGeN is a reference-guided generative pipeline for multivariate time series data that decomposes observed sequences into periodic backbone, stochastic residuals, and cross-variable dependencies to synthesize controllable synthetic data. It demonstrates that generated data can substitute for real data in forecasting tasks, outperforming prior synthetic data generators.
GenesisFunc is an automated multi-agent pipeline for generating high-quality, diverse synthetic training data for function-calling in LLMs. Fine-tuning an 8B model on this data achieves strong in-domain and out-of-domain performance, rivaling some API-based models.
This paper introduces TabKG, a knowledge-graph-guided framework for generating logically consistent synthetic supply chain tabular data. It uses an LLM ensemble to discover operational dependencies and a latent diffusion model to generate independent columns, achieving high logical consistency while preserving statistical fidelity.
Hugging Face shared slides detailing how they generated 1 trillion tokens of synthetic data for training foundation models.
This paper proposes Multi-Stage In-Flight Rejection (MSIFR), a training-free framework that reduces token waste in LLM-based synthetic data generation by detecting and terminating low-quality generation trajectories at intermediate checkpoints. Across five models and seven benchmarks, MSIFR reduces token consumption by 11–77% as a standalone method and up to 78.2% when combined with early-exit methods, while preserving or improving accuracy.
Abliteration launches a made-to-order synthetic training data workflow that generates negative, rare, and adversarial examples for classifiers, with schema, real-world facts, labels, provenance, and export to platforms like Hugging Face.
GraphGen is a knowledge-graph-guided framework for generating synthetic QA data to improve supervised fine-tuning of LLMs, targeting knowledge gaps with multi-hop sampling and style-controlled generation. Experiments show it outperforms conventional synthetic data methods.