"We generate synthetic data" is a phrase that means very different things depending on what type of data you're talking about. When a team says they used an LLM to produce synthetic training examples for a text classifier, and another team says they used a Gaussian copula to generate synthetic customer records for a churn model, they are doing something so different in mechanism, evaluation, and tradeoff that the shared vocabulary causes real confusion about what to use and when.
We spend most of our time at Synthpylon working with tabular data, which puts us squarely on one side of this distinction. But we regularly talk to teams who are trying to decide between approaches, so it is worth being precise about where the methods differ and why those differences matter for tool selection.
What Makes Tabular Generation Distinct
Tabular data is structured. Each row is an observation, each column is a typed feature with a known range, and the statistical problem is to capture the joint distribution over all those columns accurately enough that a model trained on synthetic rows generalizes to real ones.
The core modeling challenge for tabular synthesis is multivariate dependency. Age and income are correlated. Diagnosis code and prescription code are correlated. Transaction amount and account tenure are correlated. A synthetic dataset that ignores these correlations produces rows that look reasonable in isolation but fail in modeling because the feature interactions that the model learns from are missing or inverted.
This is why the standard tools for tabular synthesis are probabilistic models that explicitly capture dependency structure. A Gaussian copula models the marginal distributions of each column separately and then uses a copula function to model the correlation structure between them. CTGAN (Conditional Tabular GAN) uses a conditional generator trained to produce rows conditioned on a target column, with a mode-specific normalization step to handle the multi-modal distributions that are common in real tabular data. Both approaches are fundamentally different from anything LLM-based because they operate on structured statistical objects, not on sequences of tokens.
The evaluation metrics for tabular synthesis reflect this structure: marginal distribution similarity per column, pairwise correlation matrix fidelity, and TSTR (train on synthetic, test on real) accuracy delta compared to a model trained on real data. These are quantitative, directly measurable, and specific to the tabular domain.
What Makes Text Generation Distinct
Text synthetic data means generating plausible natural language samples that can train or fine-tune NLP models. The dominant method right now is using a large language model: prompt it with a task description and a few examples, generate a large number of candidate samples, filter for quality, and use the resulting dataset for downstream training.
The statistical problem here is fundamentally different. Text exists in a very high-dimensional token space with complex long-range dependencies. There is no equivalent of a "correlation matrix" between columns. Quality evaluation is largely human or model-in-the-loop: does this sentence sound natural? Is this customer service response factually correct? Does this medical note use appropriate clinical terminology?
LLM-based text synthesis has a distinct failure mode that tabular synthesis doesn't: hallucination. An LLM generating synthetic clinical notes might produce notes that sound realistic but describe procedures that don't correspond to the diagnosis, or reference drug interactions that are clinically implausible. The stylistic surface is fine; the semantic content is wrong. Tabular synthesis doesn't have this problem in the same way because the constraints are explicit and numeric: a generated row either satisfies the constraint that age is non-negative or it doesn't.
Privacy properties also differ. For tabular data, if you've correctly modeled marginal distributions and correlations without memorizing training rows, the individual records in the source data are not recoverable from the synthetic output. For LLM-generated text, there is evidence that LLMs can memorize and reproduce verbatim sequences from training data, which creates a different and more complex privacy risk profile.
Where Teams Get Confused
The confusion usually happens in one of two directions.
The first: a team has tabular data and hears "LLMs can generate synthetic data," so they try to prompt an LLM to produce synthetic rows in CSV format. This often produces rows that look plausible in isolation but have poor distributional fidelity. The LLM doesn't know the correlation structure of your specific dataset. It might generate realistic-looking age values and realistic-looking income values, but the joint distribution won't match your source. A churn model trained on this data will learn the wrong feature interactions.
The second confusion: a team has a text classification task, encounters tabular synthesis tools, and tries to apply them to generate text. Tabular tools are built around typed, bounded columns. Natural language is neither typed nor bounded in the same sense. You cannot fit a Gaussian copula over a column of free-text customer reviews.
We're not saying either approach is wrong in its domain. LLM-based text synthesis is a legitimate and useful technique for the NLP use cases it was designed for. Tabular synthesis tools are legitimate and useful for structured data. The mistake is importing one method into the domain of the other.
The Mixed-Schema Problem
Many real datasets are neither purely tabular nor purely text. A customer database might have structured columns (age, account balance, tenure) and free-text columns (support notes, complaint descriptions, email excerpts). What do you do with that?
The approach that works in practice is to treat the two types of columns separately. For structured columns, apply tabular synthesis to capture the statistical structure. For free-text columns, either apply an LLM-based approach to generate plausible text conditioned on the structured context, or replace the text column with a structured embedding or summary if the downstream model doesn't need raw text.
This hybrid approach is awkward and requires careful coordination to preserve correlations between structured columns and text content. If a support note should reflect the sentiment implied by the complaint code in the structured columns, you need to condition the text generation on that structured context. This is a solvable problem, but it requires explicit design decisions that a purely tabular tool or purely text tool won't make for you.
Evaluating the Right Things
One practical implication of the tabular/text distinction: evaluation pipelines need to match the data type.
For tabular synthetic data, your quality check should include Jensen-Shannon divergence per column (how close are the synthetic marginals to the real marginals), Spearman correlation matrix comparison (are column relationships preserved), and a TSTR benchmark on your specific downstream task. These are objective and automatable.
For text synthetic data, you need human review or model-based quality scoring. Human review is expensive and slow. Model-based scoring (does a separately trained classifier judge this as plausible?) is faster but introduces a circularity problem if you're evaluating data intended to train a similar model.
Teams that conflate tabular and text generation often apply the wrong evaluation approach. Trying to evaluate LLM-generated synthetic tabular data with marginal distribution tests will sometimes pass if the LLM happened to pick realistic values, but it misses the joint distribution problem. Trying to evaluate tabular-generated text with distribution tests doesn't make sense because text doesn't have the same distributional structure as numeric columns.
What Synthpylon Focuses On
We build for tabular synthesis. The problems we are set up to solve are correlation-preserving row generation, schema-consistent output with proper types and constraints, minority class oversampling while preserving joint distributions, and integration into ML pipelines where the downstream task is structured prediction.
We are not the right tool for generating synthetic training text for NLP models. If that's your problem, look at LLM-based data augmentation approaches. If your problem is structured records that you can't access due to privacy constraints, class imbalance in tabular features, or insufficient real data for training tabular models, that's the space where Gaussian copula and conditional GAN approaches will give you better results than anything LLM-based, and better privacy guarantees to boot.
Knowing which problem you're actually trying to solve is most of the battle.