Fidelity, Diversity, and Privacy: A Multi-Dimensional LLM Evaluation for Clinical Data Augmentation

Cristian Ramirez-Atencia; Enrique Baca-Garcia; Gema Bello-Orgaz; Guillermo Iglesias; Mar\'ia Navas-Loro; Merc\`e Salvador Robert

REVIEW 3 major objections 1 minor 1 cited by

Fidelity, Diversity, and Privacy: A Multi-Dimensional LLM Evaluation for Clinical Data Augmentation

T0 review · 3 major / 1 minor · reviewed 2026-05-07 · grok-4.3

Challenge this review Download PDF Read on arXiv ↗

Pith's one-line read LLMs can generate clinically coherent synthetic mental health reports that remain diverse and free of privacy leaks.

desk verdict LLM-generated synthetic mental health reports pass basic fidelity, diversity, and privacy checks but lack validation on actual downstream task performance. read the letter →

arxiv 2604.27014 v1 submitted 2026-04-29 cs.LG cs.CR

Guillermo Iglesias , Gema Bello-Orgaz , María Navas-Loro , Cristian Ramirez-Atencia , Mercè Salvador Robert , Enrique Baca-Garcia This is my paper · ORCID

classification cs.LGcs.CR

keywords syntheticdataLLMevaluationclinicalaugmentationmentalhealthprivacyICD-10naturallanguageprocessing

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 tests whether large language models can produce useful synthetic diagnostic reports for mental health cases when given only ICD-10 codes as input. It applies three different models and measures each output set along three axes: how closely the meaning matches real clinical text, how much the wording varies across samples, and whether any private patient details or copied phrases appear. The results show that the generated reports meet all three standards, offering a way to enlarge scarce training collections for clinical language tasks while complying with rules that limit access to real records.

What carries the argument

A multi-dimensional evaluation that scores generated reports for semantic fidelity to real examples, lexical diversity across the set, and absence of privacy or plagiarism signals.

What would settle it

Train a standard clinical NLP model on the synthetic reports alone, then measure its accuracy on held-out real patient reports; if performance stays flat or drops relative to training on real data, or if any privacy breach is later found in the generated text, the claim does not hold.

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Extended reading notes

Core claim

DeepSeek-R1, OpenBioLLM-Llama3, and Qwen 3.5 each produce diagnostic texts that preserve the clinical intent of given ICD-10 codes, exhibit varied lexical choices, and trigger no detectable plagiarism or privacy violations, thereby supplying additional training material for clinical natural language processing without exposing patient data.

Load-bearing premise

The three chosen metrics serve as reliable stand-ins for whether the synthetic reports will actually improve performance on real clinical tasks and will not create privacy risks in practice.

Editorial extensions

If this is right

Synthetic reports can increase the size of training sets for mental health NLP tasks without new data collection.
Privacy regulations that block sharing of real records can be navigated by substituting generated text.
Multiple LLMs produce similarly usable outputs, reducing dependence on any single model.
The same prompting approach can be reused for new ICD-10 codes as they appear.

Reading between the lines

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

Mixing the synthetic reports with limited real data during training may produce stronger models than either source alone.
Applying the same generation and checking process to other medical specialties would test how far the method travels.
Running the generated reports through downstream diagnostic classifiers on real test cases would give a direct measure of added value.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The main takeaway is that this paper conditions three LLMs on ICD-10 codes to produce synthetic mental health reports and then scores the outputs on semantic fidelity, lexical diversity, and privacy/plagiarism checks. It treats data scarcity in clinical mental health as the core problem and offers a practical pipeline as one response. That setup is reasonable and directly relevant to a known bottleneck. The authors test multiple models rather than relying on a single one, which adds a bit of robustness to the comparison. The evaluation framework itself is straightforward and hits the three dimensions that matter most for synthetic clinical text. Those choices make the work easy to understand and replicate at a basic level. The soft spot is that the central claim about expanding usable training data rests entirely on proxy metrics. Semantic similarity to real reports does not establish diagnostic accuracy or clinical utility. Lexical diversity scores do not guarantee coverage of rare presentations. Privacy checks based on overlap or basic detection do not rule out subtler leakage risks. Most critically, the paper does not report any experiment training a downstream model on the augmented data and measuring lift on held-out real cases. Without that link, it is difficult to know whether the generated reports are actually helpful in practice. This is the sort of incremental methods paper that belongs in a clinical NLP venue or a data-augmentation workshop. Readers working on healthcare data scarcity will find the concrete setup and model choices useful as a reference point. It deserves peer review because the application is timely and the multi-dimensional evaluation is a step in the right direction, even if the claims need tighter grounding in task performance.

Referee Report

3 major / 1 minor

Summary. The paper proposes using LLMs (DeepSeek-R1, OpenBioLLM-Llama3, Qwen 3.5) to generate synthetic mental health evaluation reports conditioned on ICD-10 codes, addressing data scarcity and privacy constraints in clinical NLP. It introduces a three-dimensional evaluation framework covering semantic fidelity, lexical diversity, and privacy/plagiarism checks, and claims that all models produce clinically coherent, diverse, and privacy-safe outputs that can significantly expand usable training data without compromising confidentiality.

Significance. If the central claims hold after adding quantitative validation, the work could offer a practical template for multi-dimensional LLM-based data augmentation in privacy-sensitive clinical domains such as mental health, where annotated data is scarce. The choice of domain-specific models and conditioning on ICD-10 codes is a constructive starting point, but the current absence of downstream task results limits immediate impact.

major comments (3)

[Abstract] Abstract: The assertion that the generated reports are 'clinically coherent, diverse, and privacy-safe' and 'significantly expanding the available training data' is unsupported by any quantitative scores, baseline comparisons, statistical tests, or effect sizes for the three evaluation dimensions.
[Evaluation framework] Evaluation framework: No experiments are described that train a downstream clinical NLP model (e.g., ICD-10 classifier or diagnosis predictor) on real versus augmented data and measure performance lift on held-out real clinical text; proxy metrics alone do not establish utility or rule out subtle memorization risks.
[Privacy/plagiarism assessment] Privacy/plagiarism assessment: The specific methods used (n-gram overlap, membership inference, or attribute inference) are not detailed, nor are they validated against realistic confidentiality threats that could still arise in deployment.

minor comments (1)

[Abstract] The abstract would benefit from a brief statement of the dataset size, number of generated samples per model, and exact metric definitions (e.g., embedding model for semantic fidelity).

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important areas for strengthening the presentation of quantitative results, the scope of validation, and the transparency of privacy methods. We address each point below and have made revisions to improve the paper accordingly.

read point-by-point responses

Referee: [Abstract] Abstract: The assertion that the generated reports are 'clinically coherent, diverse, and privacy-safe' and 'significantly expanding the available training data' is unsupported by any quantitative scores, baseline comparisons, statistical tests, or effect sizes for the three evaluation dimensions.

Authors: We agree that the abstract would benefit from explicit quantitative support. The body of the manuscript already reports concrete metrics (semantic similarity for fidelity, distinct n-gram ratios and type-token ratio for diversity, and n-gram overlap plus membership inference success rates for privacy) along with baseline comparisons to non-conditioned generation and statistical tests (e.g., paired t-tests). We have revised the abstract to include representative scores, effect sizes, and a brief mention of the statistical comparisons while preserving its length. revision: yes
Referee: [Evaluation framework] Evaluation framework: No experiments are described that train a downstream clinical NLP model (e.g., ICD-10 classifier or diagnosis predictor) on real versus augmented data and measure performance lift on held-out real clinical text; proxy metrics alone do not establish utility or rule out subtle memorization risks.

Authors: The manuscript's primary contribution is the three-dimensional evaluation framework itself rather than an end-to-end demonstration of downstream utility. We acknowledge that proxy metrics do not fully substitute for task-specific gains and have added a dedicated limitations subsection that discusses this gap, outlines how the synthetic reports could be integrated into classifier training, and reports a small-scale proxy experiment (ICD-10 code prediction accuracy on a held-out synthetic test set). Full downstream experiments on real clinical text would require additional IRB-approved data access that was outside the current scope; we note this explicitly as a direction for future work. revision: partial
Referee: [Privacy/plagiarism assessment] Privacy/plagiarism assessment: The specific methods used (n-gram overlap, membership inference, or attribute inference) are not detailed, nor are they validated against realistic confidentiality threats that could still arise in deployment.

Authors: We have expanded the privacy section to include precise methodological details: 5-gram overlap thresholds, the shadow-model architecture and training procedure for membership inference, and the attribute-inference pipeline that attempts to recover ICD-10 codes and demographic markers. We also report results from simulated deployment threats (e.g., reconstruction attacks on the generated reports) and compare them against a real-data baseline. These additions clarify the evaluation and address potential residual risks. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical evaluation on external proxy metrics

full rationale

The paper presents an empirical methodology for generating synthetic mental health reports with LLMs and evaluates them directly on three standard dimensions (semantic fidelity via embeddings, lexical diversity via n-gram statistics, and privacy via overlap/plagiarism checks). No equations, fitted parameters, or derivations are described that reduce to the inputs by construction. The assessment framework relies on independent, externally defined metrics rather than self-referential definitions or self-citation chains that bear the central claim. This is a non-derivational empirical study whose claims rest on observable outputs compared to real data, satisfying the default expectation of no circularity.

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

The work rests on the unstated premise that LLM-generated text can be meaningfully scored for clinical coherence and privacy using existing NLP metrics; no new axioms or entities are introduced.

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Cite this review

Pith. "Pith review of Fidelity, Diversity, and Privacy: A Multi-Dimensional LLM Evaluation for Clinical Data Augmentation." pith.science (2026). https://pith.science/paper/2604.27014

@misc{pith2026260427014,
  author       = {Pith},
  title        = {Pith review of: Fidelity, Diversity, and Privacy: A Multi-Dimensional LLM Evaluation for Clinical Data Augmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2604.27014}},
  note         = {Machine review of arXiv:2604.27014}
}

read the original abstract

The scarcity of high-quality annotated medical data, particularly in mental health, poses a significant bottleneck for training robust machine learning models. Privacy regulations restrict data sharing, making synthetic data generation a promising alternative. The use of Large Language Models (LLMs) in a data augmentation pipeline could be leveraged as an alternative in this field. In the proposed methodology, DeepSeek-R1, OpenBioLLM-Llama3 and Qwen 3.5 are used to generate synthetic mental health evaluation reports conditioned on specific International Classification of Diseases, Tenth Revision (ICD-10) codes. Because naive text generation can lead to mode collapse or privacy breaches (memorization), a comprehensive evaluation framework is introduced. The generated diagnostic texts are assessed across three dimensions: semantic fidelity, lexical diversity, and privacy/plagiarism. The results demonstrate that all models can generate clinically coherent, diverse, and privacy-safe synthetic reports, significantly expanding the available training data for clinical natural language processing tasks without compromising patient confidentiality.

Figures

Figures reproduced from arXiv: 2604.27014 by the authors.

**Figure 1.** Latent representation of real and synthetic texts using the embeddings produced with view at source ↗

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies
cs.CL 2026-08 conditional novelty 6.0 of 10

Synthetic clinical communication generated by LLMs can train clinical NLP models in thirteen case studies, but only one is tested on real patient text, leaving transfer to authentic communication unproven.

Reference graph

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