An LLM-Based Framework for Synthetic Data Generation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The demand for high-quality datasets is rapidly increasing across sectors such as healthcare, finance, and cybersecurity, yet challenges like data scarcity and privacy concerns persist. To address this, we introduce a framework for synthetic data generation that empowers users to create realistic datasets while maintaining privacy. The framework leverages fine-tuned Large Language Models (LLMs) and differential privacy techniques, including IBM's diffprivlib, to generate synthetic data that replicates real-world patterns without exposing sensitive information. A proof-of-concept platform has been constructed to facilitate seamless data generation and augmentation, making it particularly useful in scenarios where original datasets are inaccessible, scarce, or privacy-restricted. The platform supports the creation of datasets across five key categories, employing advanced methods to preserve data integrity while ensuring compliance with stringent privacy standards. By combining cutting-edge AI technologies with robust privacy-preserving techniques, this framework offers a practical solution for researchers and professionals seeking reliable synthetic data to drive innovation in data-sensitive fields.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it