TAGAL: Tabular Data Generation using Agentic LLM Methods

Beno\^it Ronval; Pierre Dupont; Siegfried Nijssen

REVIEW 4 major objections 5 minor 26 references

TAGAL: Tabular Data Generation using Agentic LLM Methods

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read An agentic feedback loop between two LLMs can generate synthetic tabular data whose downstream utility matches fine-tuned generative models, without any LLM training.

desk verdict TAGAL is a plausible training-free LLM pipeline for tabular generation, but the abstract overclaims and the feedback-loop ablation is missing. read the letter →

arxiv 2509.04152 v1 pith:5TL3NVC5 submitted 2025-09-04 cs.LG cs.AI

Benoît Ronval , Pierre Dupont , Siegfried Nijssen This is my paper

classification cs.LGcs.AI

keywords tabulardatagenerationagenticLLMsyntheticin-contextlearningfeedbacklooptraining-freeutilitycontamination

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes TAGAL, a family of training-free methods that use two large language models to generate synthetic tabular data: one produces rows from few-shot examples, the other critiques those rows and feeds recommendations back into the generator over several iterations. The claim is that this agentic loop lets an off-the-shelf LLM match the utility of state-of-the-art generative models that are fine-tuned on the real data, while generally beating other training-free LLM approaches such as EPIC. The authors evaluate on four datasets with downstream classifiers and distributional similarity metrics, and show that the methods work even on a dataset unlikely to have been seen during LLM pretraining. If true, the result matters because it suggests high-quality synthetic tables can be produced without training or specialized infrastructure, and that expert knowledge can be injected through prompts.

What carries the argument

The central mechanism is the agentic feedback loop: a generation LLM is given an initial prompt containing feature names, types, and dataset statistics (mean, median, standard deviation, category frequencies) plus few-shot examples grouped by class, and it outputs CSV-serialized rows. A second feedback LLM then criticizes those rows step by step, listing strengths and weaknesses, and the recommendations are appended to the generation conversation history. After a fixed number of iterations (default three), the accumulated history is either reused directly (ReducedLoop) or summarized by a third LLM into a refined prompt (Prompt-Refine) to generate the final batch. The loop is what distinguish

What would settle it

A controlled ablation that runs SynthLoop with the same initial prompt but no feedback iterations, or with a trivial random critic, would settle whether the loop is load-bearing. If TSTR utility and precision/recall do not drop when feedback is removed, the central claim collapses.

Watch

Extended reading notes

Core claim

The paper's central claim is that an agentic workflow—one LLM generating CSV-style rows from class-grouped few-shot examples and a second LLM producing structured criticism that is fed back into the generation loop—can produce synthetic tabular data whose downstream classification utility is on par with trained models like GReaT, Tabula, CTGAN, and TabDDPM, and is generally higher than EPIC, a training-free prompt-only baseline. Across Adult, Bank, German, and Thyroid, the best TAGAL variant, Prompt-Refine, reaches TSTR ROC AUC scores close to or above fine-tuned baselines, especially on Thyroid, while the feedback process sharply reduces collisions relative to EPIC. The Thyroid results, on

Load-bearing premise

The paper assumes that the iterative critic feedback, rather than the extra dataset statistics already present in TAGAL's initial prompt, is what produces the gains over EPIC.

Editorial extensions

If this is right

Training-free synthetic tabular generation becomes a realistic alternative to fine-tuned models, lowering hardware and data-preparation barriers.
The feedback loop corrects distributional flaws and reduces copying of few-shot examples, so generated data is more genuinely synthetic than EPIC's output.
Prompt-injected external knowledge can extend generation to domains the LLM has not seen during pretraining, as demonstrated on the Thyroid dataset.
Scaling the LLM inside the loop improves utility, but open 8B models remain competitive, so the method is portable to modest hardware.
Combining synthetic with real data can match or exceed real-only training, making the method useful for data augmentation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

We infer that the reported gains over EPIC may partly come from TAGAL's initial prompt, which includes dataset statistics that EPIC's prompt lacks; the paper does not ablate feedback away, so the loop's marginal contribution is untested.
An untested extension is using example-selection strategies like TabGen-ICL's to choose few-shots, which could further reduce collisions and improve diversity.
We infer the feedback loop's benefit should grow with LLM reasoning ability and with harder tabular distributions; comparing loop depth and critic strength on more datasets would reveal the scaling.
A testable application: the same loop could enforce constraints such as privacy or conditional generation by changing only the feedback prompt, without retraining.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper proposes TAGAL, a training-free agentic LLM pipeline for tabular data generation. The combination of EPIC-style few-shot serialization, a separate LLM critic, and prompt summarization is new, and the authors share their code. That said, the headline claim of parity with trained models holds only on Adult and Thyroid when using the default Llama 3.1 8B; on Bank and German it clearly falls short (e.g., Prompt-Refine TSTR 0.59 vs Tabula 0.86 on Bank). So the abstract overstates what the default setup delivers.

What the paper does well: the Thyroid dataset is post-cutoff and addresses contamination head-on; the authors report collisions and run prompt variants, which is more than most tabular generation papers do. The Prompt-Refine idea—distilling the feedback history into a single prompt—is a practical answer to the token cost of iterative loops.

The main soft spot is the missing ablation. The paper attributes gains to the agentic feedback loop, but SynthLoop's initial prompt already includes dataset statistics that EPIC does not have. Table 6 removes the statistics while keeping the loop; there is no no-feedback condition with the same prompt. So you cannot isolate whether the critic does any work. The stress-test note is right. That said, the no-info condition still beats EPIC, which suggests the feedback loop may not be inert—but the paper should prove that directly.

Second, there are no error bars anywhere. Every number is a single average, despite the high variance of LLM sampling. Averaging over five classifier runs is not the same as averaging over generation seeds. This weakens the comparative claims.

Third, the abstract says the method performs on par with training-requiring approaches. Table 3 contradicts this for Bank and German with Llama. With GPT-4o the results improve, but the default setup does not support the statement as written.

Overall: it is a decent workshop paper with a plausible method and a clear evaluation, but the central causal claim about the feedback loop is under-supported. A serious referee would ask for the ablation and error bars before accepting. I would engage with it if those are fixed; as is, I would not cite it as a reliable result.

Referee Report

4 major / 5 minor

Summary. The paper introduces TAGAL, a family of three training-free LLM-based methods for synthetic tabular data generation: SynthLoop (an iterative generation/feedback loop between two LLMs), ReducedLoop (reuse of the conversation history to reduce cost), and Prompt-Refine (use of a third LLM to summarize the feedback loop into a refined prompt). The methods are evaluated on Adult, Bank, German, and Thyroid using downstream classification utility (TSTR and combined) and precision/recall, and are compared against CTGAN, TabDDPM, GReaT, Tabula, EPIC, and a statistical baseline. The central claim is that TAGAL achieves utility on par with training-based generative models and generally outperforms training-free approaches, with the agentic feedback loop as the primary source of improvement. The paper also studies prompt variants and meta-parameters.

Significance. The topic is timely, and the training-free agentic framework is a plausible alternative to fine-tuned LLM generators. The paper's strengths include a model-agnostic design, a public code repository, and the thoughtful use of a post-cutoff dataset (Thyroid) as a contamination control. The Prompt-Refine idea of summarizing the feedback loop into a single prompt is original and potentially useful. If the central claim were substantiated, the work would be a meaningful contribution to LLM-based tabular data generation. However, the current evidence does not establish the claim: the comparison with EPIC is confounded by the enriched initial prompt, no uncertainty quantification is provided, the reported results are inconsistent with the 'generally outperforms' assertion when the default Llama 3.1 8B model is used, and the paper does not compare with other cited training-free methods such as MALLM-GAN and TabGen-ICL.

major comments (4)

[3.1 vs 2.2, Table 6] The comparison with EPIC in Table 3 conflates two changes: SynthLoop's initial prompt includes feature types, category distributions, and means/medians/stds (Section 3.1), whereas EPIC's prompt contains only few-shot examples (Section 2.2). Table 6 varies whether this dataset information is present (Info vs No Info) but always retains the feedback loop; it never removes the feedback loop while keeping the same initial prompt. Without a no-feedback ablation (or adding the same statistics to EPIC), the reported gains over EPIC cannot be attributed to the agentic feedback loop. Since the abstract and Section 1 present the agentic workflow as the key novelty, this is a load-bearing omission.
[Section 5.1, Table 3] No measure of variability is reported. TSTR and combined utilities are averages over 5 classifier runs, but the text does not give standard deviations or confidence intervals, and the generation itself is stochastic (temperature 0.7) with no repeated generation runs. On Bank, Prompt-Refine TSTR is 0.59 while EPIC is 0.66; on Thyroid, SynthLoop and ReducedLoop are 0.95 and 0.91 versus EPIC's 0.98. These differences are material, yet with no error bars it is impossible to tell whether they are meaningful. The claims of being 'on par' and 'generally outperforming' are therefore not statistically established.
[Section 5.1, Table 3] The paper states that 'the best method from TAGAL always outperforms EPIC' and concludes that TAGAL 'generally outperforms other training-free approaches'. This best-of-three selection is not a fair comparison: with Llama 3.1 8B, ReducedLoop and Prompt-Refine underperform EPIC on Bank (0.49 and 0.59 vs 0.66), and all three TAGAL methods are at or below EPIC on Thyroid (0.95, 0.91, 0.98 vs 0.98). A fixed model-selection rule or a per-method comparison is needed before such a claim can be made.
[Section 4.4, Table 5] The number of feedback iterations is fixed at 3, and the paper does not ablate this choice. Since the feedback loop is the central claimed contribution, the authors should show that performance improves with the number of iterations or, at minimum, that one iteration outperforms zero (no-feedback). The current experiments (Tables 5 and 6) vary temperature, few-shot count, feature order, and prompt content, but never the presence or number of feedback iterations. This leaves the mechanism underlying the method's performance unexamined.

minor comments (5)

[Captions, Tables 3-6] The numbers beside dataset names (e.g., Adult (1108), Adult (1398), Adult (1797), Adult (2098)) are not explained. The caption says they indicate the size of training sets for downstream utilities, but the values differ across tables for the same dataset. Please clarify what these numbers represent and why they change.
[Section 4.2] The precision/recall definitions use a manifold represented by spheres with radius to the kth nearest neighbor. Please specify how distance is computed in a mixed categorical/numerical feature space.
[Section 3.3] The sentence 'the quality of the generated examples largely depends on the quality of the refined prompt generated by the feedback LLM' should refer to the summary LLM, not the feedback LLM.
[Section 2.2] MALLM-GAN is described as using a logistic regression to distinguish real from generated data, which requires training on the data. Calling it 'training-free' is imprecise.
[General] The exact prompt templates are not included. Providing them in an appendix would improve reproducibility, especially for the feedback and summary prompts.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the evaluation is held-out, all generation uses training data, and the only self-citation is peripheral.

full rationale

The paper's central claim, that TAGAL generates synthetic tabular data with utility comparable to trained models and better than other training-free approaches, is evaluated on held-out test sets. The generation process uses only training splits (Section 4.1: 'Each dataset is split into 80% training and 20% test sets before the generation part'), and the downstream utility metrics (TSTR and combined) are computed on test data. No parameter is fitted to the test set and then renamed a prediction; the synthetic data are not constructed to satisfy the evaluation metric by definition. The only self-citation is [19], the authors' contamination detection paper, cited in Section 2.3 for the general possibility of LLM contamination and mentioned in Section 5.1 when discussing why the Thyroid dataset is unlikely to be contaminated. This citation is not load-bearing: the argument about Thyroid is based on its release date, not on the cited paper's results. The authors do not invoke any uniqueness theorem or prior work by themselves to force their method's design. The prompt variants studied in Table 6 are empirical ablations, not definitions that make the output equal to the input. The skeptical concern that the agentic feedback loop's contribution is confounded with the richer initial prompt (Section 3.1 vs Section 2.2) is an experimental design limitation, not a circularity: one could in principle run TAGAL without feedback, and the paper does not claim such an ablation. Therefore, no specific circular reduction is present, and the circularity score is 0.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

No new entities are postulated. The method relies on standard LLM capabilities and common evaluation metrics. Free parameters are typical hyperparameters, not fitted to test outcomes.

free parameters (5)

temperature = 0.7
Generation LLM sampling temperature; varied to 0.9 in one variant (Table 5).
few-shot examples per class = 20
Initial prompt few-shot count; 30 in one variant.
feedback iterations = 3
Maximal loop iterations; chosen to control generation time.
maximum generated tokens = 16,384 (generation), 2,048 (feedback)
Context/token limits for LLM calls.
k for precision/recall = 5
k-nearest neighbor parameter from Synthcity [17].

assumptions (5)

domain assumption In-context learning from few-shot tabular examples is sufficient for an LLM to generate realistic rows.
Central to all TAGAL methods; from prior work (EPIC).
domain assumption Feedback from an LLM critic improves subsequent generated data.
The claim of agentic benefit; not ablated in the paper (Section 3.1).
domain assumption The Thyroid dataset is unlikely to be in LLM training corpora due to release date.
Stated in Section 4.1; not independently verified.
standard math Precision/recall with k=5 and downstream classifier AUC are valid measures of synthetic tabular quality.
Borrowed from prior literature (Synthcity, Sajjadi et al.).
domain assumption LLM-generated data can be evaluated directly with standard classifiers without formatting leakage.
Assumed in the TSTR protocol (Section 4.2).

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Cite this review

Pith. "Pith review of TAGAL: Tabular Data Generation using Agentic LLM Methods." pith.science (2026). https://pith.science/paper/5TL3NVC5

@misc{pith2026250904152,
  author       = {Pith},
  title        = {Pith review of: TAGAL: Tabular Data Generation using Agentic LLM Methods},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5TL3NVC5}},
  note         = {Machine review of arXiv:2509.04152}
}

read the original abstract

The generation of data is a common approach to improve the performance of machine learning tasks, among which is the training of models for classification. In this paper, we present TAGAL, a collection of methods able to generate synthetic tabular data using an agentic workflow. The methods leverage Large Language Models (LLMs) for an automatic and iterative process that uses feedback to improve the generated data without any further LLM training. The use of LLMs also allows for the addition of external knowledge in the generation process. We evaluate TAGAL across diverse datasets and different aspects of quality for the generated data. We look at the utility of downstream ML models, both by training classifiers on synthetic data only and by combining real and synthetic data. Moreover, we compare the similarities between the real and the generated data. We show that TAGAL is able to perform on par with state-of-the-art approaches that require LLM training and generally outperforms other training-free approaches. These findings highlight the potential of agentic workflow and open new directions for LLM-based data generation methods.

Figures

Figures reproduced from arXiv: 2509.04152 by the authors.

**Figure 1.** Overview of the SynthLoop method to generate tabular data. SynthLoop includes two LLMs, one generating the new examples and one providing feedback. any LLM, closed or open-source, may be used. Furthermore, the use of LLMs and the absence of training allow the methods from TAGAL to work with limited amount of data thanks to the large number of parameters of these models that allows for in-context learning with few-… view at source ↗

**Figure 2.** Overview of the ReducedLoop and Prompt-Refine approaches. The Feedback Loop part is the iterative process shown in figure 1. Parts between dotted lines are only executed for Prompt-Refine. third LLM, called summary LLM, to create a new prompt. This LLM receives the entire generation’s conversation history and is tasked to output a prompt summarizing the important information which will be used to generate other tabu… view at source ↗

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Reference graph

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Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

arXiv preprint arXiv:2210.06280 (2022)

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