@inproceedings{kuznechenko-etal-2026-tabautosyn,
title = "{T}ab{A}uto{S}yn: A Generator-Agnostic Pipeline for Deployment-Ready Synthetic Tabular Data",
author = "Kuznechenko, Dmitrii and
Rapatskikh, Kirill and
Kalyuzhnaya, Anna and
Boukhanovsky, Alexander",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.240/",
pages = "4048--4066",
ISBN = "979-8-89176-460-6",
abstract = "Synthetic tabular data lets organisations share, rebalance, and stress-test sensitive in-house tables-but only if the result is actually fit for the downstream task. Yet a table that matches the real distribution on aggregate statistics can still be unfit for its downstream task: it loses the rare cases that matter, is not optimised for the end task, and silently breaks the deterministic relations that domain data must obey. We present TabAutoSyn, an end-to-end, generator-agnostic pipeline that turns any base generator into deployment-ready synthetic data through four automated stages: generate, verify, augment, and curate. A multi-agent LLM module discovers functional dependencies and domain constraints from the real table and repairs or removes violating rows; a divergence-minimising stage rebuilds under-represented tails; and a curation stage provably never lowers a chosen downstream score. Across 22 public and proprietary datasets and eleven generators spanning five model families (GAN, VAE, diffusion, language-model, and graph), TabAutoSyn delivers the best train-on-synthetic utility and the highest validity while preserving privacy. Because it is generator-agnostic, it attains these results on a commodity GPU at a fraction of the cost of the strongest LLM-based baselines, and it needs no per-dataset tuning-making it practical to run on sensitive, in-house data."
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<abstract>Synthetic tabular data lets organisations share, rebalance, and stress-test sensitive in-house tables-but only if the result is actually fit for the downstream task. Yet a table that matches the real distribution on aggregate statistics can still be unfit for its downstream task: it loses the rare cases that matter, is not optimised for the end task, and silently breaks the deterministic relations that domain data must obey. We present TabAutoSyn, an end-to-end, generator-agnostic pipeline that turns any base generator into deployment-ready synthetic data through four automated stages: generate, verify, augment, and curate. A multi-agent LLM module discovers functional dependencies and domain constraints from the real table and repairs or removes violating rows; a divergence-minimising stage rebuilds under-represented tails; and a curation stage provably never lowers a chosen downstream score. Across 22 public and proprietary datasets and eleven generators spanning five model families (GAN, VAE, diffusion, language-model, and graph), TabAutoSyn delivers the best train-on-synthetic utility and the highest validity while preserving privacy. Because it is generator-agnostic, it attains these results on a commodity GPU at a fraction of the cost of the strongest LLM-based baselines, and it needs no per-dataset tuning-making it practical to run on sensitive, in-house data.</abstract>
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%0 Conference Proceedings
%T TabAutoSyn: A Generator-Agnostic Pipeline for Deployment-Ready Synthetic Tabular Data
%A Kuznechenko, Dmitrii
%A Rapatskikh, Kirill
%A Kalyuzhnaya, Anna
%A Boukhanovsky, Alexander
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F kuznechenko-etal-2026-tabautosyn
%X Synthetic tabular data lets organisations share, rebalance, and stress-test sensitive in-house tables-but only if the result is actually fit for the downstream task. Yet a table that matches the real distribution on aggregate statistics can still be unfit for its downstream task: it loses the rare cases that matter, is not optimised for the end task, and silently breaks the deterministic relations that domain data must obey. We present TabAutoSyn, an end-to-end, generator-agnostic pipeline that turns any base generator into deployment-ready synthetic data through four automated stages: generate, verify, augment, and curate. A multi-agent LLM module discovers functional dependencies and domain constraints from the real table and repairs or removes violating rows; a divergence-minimising stage rebuilds under-represented tails; and a curation stage provably never lowers a chosen downstream score. Across 22 public and proprietary datasets and eleven generators spanning five model families (GAN, VAE, diffusion, language-model, and graph), TabAutoSyn delivers the best train-on-synthetic utility and the highest validity while preserving privacy. Because it is generator-agnostic, it attains these results on a commodity GPU at a fraction of the cost of the strongest LLM-based baselines, and it needs no per-dataset tuning-making it practical to run on sensitive, in-house data.
%U https://aclanthology.org/2026.emnlp-industry.240/
%P 4048-4066
Markdown (Informal)
[TabAutoSyn: A Generator-Agnostic Pipeline for Deployment-Ready Synthetic Tabular Data](https://aclanthology.org/2026.emnlp-industry.240/) (Kuznechenko et al., EMNLP 2026)
ACL