Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets

• URL: http://arxiv.org/abs/2605.17758v1
• Date: Mon, 18 May 2026 02:24:52 GMT
• Title: Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets
• Abstract summary: We present Memisis, a tool that orchestrates and evaluates synthetic data.<n>Our tool creates a unified workflow for data generation, validation and evaluation.
• Score: 1.2089037589460674
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Synthetic data is widely used in healthcare to create datasets that are similar to original data but without the privacy concerns. Generating and evaluating synthetic data across privacy, utility and fairness is crucial for facilitating high quality data availability for downstream prediction tasks and clinical decision making. We present Memisis, a tool that orchestrates and evaluates synthetic data by leveraging existing synthetic data tools, the power of large language models and state-of-the-art evaluation metrics. Our tool creates a unified workflow for data generation, validation and evaluation. Users have control over the training size, training epochs and the number of synthetic rows to sample. Instead of knobs to tune synthetic data, the interactive agent allows users to specify their synthetic data generation goals and the tool will orchestrate the workflow by leveraging existing tools while performing the requisite evaluation. For the demo, we use an open source schizophrenia dataset with protected attributes related to race and gender, three different synthesizers and a local language model to orchestrate the workflow. We observe that CTGAN, TVAE and GaussianCopula have comparable performance across fairness and utility metrics. The workflow allows users flexibility and control over the data generation and evaluation process.

Related papers

• SynAE: A Framework for Measuring the Quality of Synthetic Data for Tool-Calling Agent Evaluations [18.71623023651951]
  SynAE is an evaluation framework for assessing how well synthetic benchmarks for tool-calling agents replicate and augment the characteristics of real data trajectories.<n>We evaluate SynAE using recent agent benchmarks and test common synthetic data failure modes via realistic and controlled generation schemes.
  arXiv  Detail & Related papers  (2026-05-21T14:45:02Z)
• Generate, Evaluate, Iterate: Synthetic Data for Human-in-the-Loop Refinement of LLM Judges [11.924947088975722]
  We present a tool that integrates synthetic data generation into the LLM-as-a-judge workflow.<n>It empowers users to create tailored and challenging test cases with domains, personas, lengths, and desired outcomes, including borderline cases.<n>The generated synthetic data proved as effective as hand-crafted data for both refining evaluation criteria and aligning with human preferences.
  arXiv  Detail & Related papers  (2025-11-06T15:57:19Z)
• Understanding the Influence of Synthetic Data for Text Embedders [52.04771455432998]
  We first reproduce and publicly release the synthetic data proposed by Wang et al.<n>We critically examine where exactly synthetic data improves model generalization.<n>Our findings highlight the limitations of current synthetic data approaches for building general-purpose embedders.
  arXiv  Detail & Related papers  (2025-09-07T19:28:52Z)
• Procedural Environment Generation for Tool-Use Agents [55.10427063893754]
  We introduce RandomWorld, a pipeline for the procedural generation of interactive tools and compositional tool-use data.<n>We show that models tuned via SFT and RL on synthetic RandomWorld data improve on a range of tool-use benchmarks.
  arXiv  Detail & Related papers  (2025-05-21T14:10:06Z)
• ToolFlow: Boosting LLM Tool-Calling Through Natural and Coherent Dialogue Synthesis [80.34000499166648]
  We propose a Graph-based Sampling strategy to sample more relevant tool combinations, and a Planned-generation strategy to create plans that guide the synthesis of coherent dialogues.<n>We apply SFT on LLaMA-3.1-8B using 8,000 synthetic dialogues generated with ToolFlow.<n>Results show that the model achieves tool-calling performance comparable to or even surpassing GPT-4, while maintaining strong general capabilities.
  arXiv  Detail & Related papers  (2024-10-24T05:45:04Z)
• SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data [3.360001542033098]
  SynthEval is a novel open-source evaluation framework for synthetic data. It treats categorical and numerical attributes with equal care, without assuming any special kind of preprocessing steps. Our tool leverages statistical and machine learning techniques to comprehensively evaluate synthetic data fidelity and privacy-preserving integrity.
  arXiv  Detail & Related papers  (2024-04-24T11:49:09Z)
• A Multi-Faceted Evaluation Framework for Assessing Synthetic Data Generated by Large Language Models [3.672850225066168]
  generative AI and large language models (LLMs) have opened up new avenues for producing synthetic data.<n>Despite the potential benefits, concerns regarding privacy leakage have surfaced.<n>We introduce SynEval, an open-source evaluation framework designed to assess the fidelity, utility, and privacy preservation of synthetically generated tabular data.
  arXiv  Detail & Related papers  (2024-04-20T08:08:28Z)
• Reliability in Semantic Segmentation: Can We Use Synthetic Data? [69.28268603137546]
  We show for the first time how synthetic data can be specifically generated to assess comprehensively the real-world reliability of semantic segmentation models. This synthetic data is employed to evaluate the robustness of pretrained segmenters. We demonstrate how our approach can be utilized to enhance the calibration and OOD detection capabilities of segmenters.
  arXiv  Detail & Related papers  (2023-12-14T18:56:07Z)
• Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Data [3.555830838738963]
  Differentially private (DP) synthetic data sets are a solution for sharing data while preserving the privacy of individual data providers. We identify the most effective synthetic data generation techniques for training and evaluating machine learning models.
  arXiv  Detail & Related papers  (2023-10-30T03:37:16Z)
• 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)
• 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)
• TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments [84.6017003787244]
  This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets. We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
  arXiv  Detail & Related papers  (2022-08-16T20:46:08Z)

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.