Multimodal Large Language Models for Image, Text, and Speech Data Augmentation: A Survey

Achyut Paudel; Maged Shoman; Manoj Karkee; Ranjan Sapkota; Shaina Raza

REVIEW 5 major objections 5 minor 1 cited by

Multimodal Large Language Models for Image, Text, and Speech Data Augmentation: A Survey

T0 review · 5 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This survey attempts to establish the first full map of LLM-based data augmentation across image, text, and speech, organized as per-modality pipelines, technique taxonomies, and matched limitations and solutions.

desk verdict A useful three-modality framing undercut by a non-reproducible screening process and off-topic table entries; worth engaging after revision, not as it stands. read the letter →

arxiv 2501.18648 v2 pith:Y3GA5QWL submitted 2025-01-29 cs.CV

Ranjan Sapkota , Shaina Raza , Maged Shoman , Achyut Paudel , Manoj Karkee This is my paper · ORCID

classification cs.CV

keywords dataaugmentationlargelanguagemodelsmultimodalLLMsimagetextspeechsyntheticgenerationsurvey

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

Data augmentation has shifted from hand-crafted transformations and LSTM-based generation toward context-aware synthetic data produced by multimodal large language models. This survey tries to establish that the shift can be mapped as one coherent field across image, text, and speech, and it claims to be the first review to cover all three modalities together. It organizes the recent literature into per-modality pipelines, named techniques, and paired lists of limitations and literature-sourced solutions, based on 104 studies published from 2020 onward. A sympathetic reader would care because the resulting map lets practitioners in computer vision, NLP, and audio research locate their methods, compare failure modes, and see where one modality's fixes might transfer to another.

What carries the argument

The central organizing device is a three-modality taxonomy, one branch each for image, text, and speech. Each branch pairs a step-by-step augmentation pipeline (image encoding → prompt generation → instruction generation → natural-language-to-code translation → code execution → quality assessment → metadata generation → dataset integration; text encoding → prompt generation → instruction generation → transformation → execution → quality assessment → metadata → dataset integration; speech preprocessing → feature extraction → initial augmentation → multimodal embedding/contextual understanding → synthetic speech generation → refinement/filtering → dataset integration) with a list of named techniques and a set of limitations matched to literature-sourced solutions. This three-part structure is what lets the paper treat heterogeneous, modality-specific methods as instances of one LLM-driven data augmentation practice.

What would settle it

Re-run the search described in Section 2 with exact keyword families, date ranges, and inclusion criteria across the eight databases listed there, and check whether any earlier peer-reviewed survey already covered image, text, and speech augmentation together; finding one before 2025 would refute the firstness claim.

Watch

Extended reading notes

Core claim

The paper's central claim is that LLM-based data augmentation has become a genuinely cross-modal practice and that it can be captured in a single survey covering image, text, and speech together—a scope the authors state has not been attempted before. It argues that post-2020 multimodal LLMs produce context-aware synthetic data through language-mediated steps, replacing manual transformations and LSTM-era automation. On the basis of 104 curated studies (24 image, 45 text, 35 speech), it catalogs distinct augmentation techniques and common limitations for each modality, and it collects proposed remedies from the same literature. It concludes that the field is moving toward self-augmenting systems, citing reinforcement-learning approaches as a next step.

Load-bearing premise

The load-bearing premise is that the 104 curated studies are representative and complete enough to support a first comprehensive survey covering image, text, and speech; the search protocol leaves exact queries and date ranges unspecified, so a missed prior tri-modal survey would undermine the claim.

Editorial extensions

If this is right

Researchers can place any new augmentation method into the appropriate pipeline and compare it with the named techniques already catalogued for that modality.
The limitation lists provide concrete design targets: image methods must guard against semantic misalignment, text methods against semantic drift and redundancy, speech methods against temporal distortion, timbre loss, and synthetic unrealism.
The literature-sourced solutions give practitioners ready-made starting points, such as natural-language-inference filtering for generated text and joint timbre-content modeling for speech.
If the firstness claim holds, later work on LLM-based augmentation will cite this survey as the reference map for the pre-2025 landscape.

Reading between the lines

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

Editorial inference: the taxonomy suggests that evaluation across modalities is the missing piece; if each technique reported gains normalized by data size and compute, cross-modal comparison would become possible.
Editorial inference: the recurring limitations—LLMs lacking native acoustic and visual structure—point toward hybrid architectures combining LLMs with signal-processing or vision-specific modules, a direction the paper sketches but does not assert as its own finding.
Editorial inference: the firstness claim is empirically checkable by bibliographic date-mapping; a reader could reconstruct the search with explicit queries and verify whether any earlier survey already covered all three modalities.
Editorial inference: the proposed solutions are culled from the surveyed papers rather than validated here, so their effectiveness is an open empirical question rather than a settled result.

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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 useful core here is real: a three-modality organization of LLM-based augmentation, with concrete tables and a limitation-solution mapping that a newcomer could actually navigate. The process overview figures (image, text, speech) are a decent tutorial scaffold. Giving credit where it is due, the authors did assemble a large corpus and tried to abstract common failure modes across modalities, which is more than most modality-specific surveys bother to do.

The soft spots are proportionate to how much the paper claims. The 'first comprehensive survey' claim is not supported by the methodology. Section 2.4's third inclusion question ('Does the article propose a framework, tool, or methodology?') admits essentially any paper, and the tables confirm the leak: DeepDR-LLM and Med-MLLM are diagnostic systems, OphGLM is an ophthalmology assistant, LLM-Commentator generates football commentary, LAMB is an LMS assistant, and Forged-GAN-BERT is authorship attribution. None of these generates or modifies training samples for a downstream model, which is the paper's own definition of augmentation from Section 1. That is an internal consistency problem, not just an external completeness concern. If a meaningful fraction of the 104 entries are off-topic, the 24/45/35 counts and the fifteen-technique taxonomies are built on shaky ground.

The historical background also mixes in non-augmentation work (vocal cord augmentation, teleconferencing) and the 'multimodal LLM' label is stretched to cover GANs and diffusion models without much discussion. The search protocol is not reproducible: no exact queries, date ranges, or screening logs, despite the reproducibility claim in the contributions. On citation patterns, the authors do reuse their own prior work in tables, but those are legitimately relevant to the topic and I would not flag that as misconduct.

Who is this for? A graduate student who wants a quick map of the area and is willing to check the primary sources. The paper is not yet a reliable reference because the corpus needs cleaning and the firstness claim needs qualification. I would not cite it in my own work yet.

Recommendation: send it to peer review, but with a heavy revision request: tighten the inclusion criteria, re-screen the tables, and either drop or carefully scope the 'first comprehensive' claim. A serious referee could turn this into a useful survey.

Referee Report

5 major / 5 minor

Summary. This manuscript is a survey of recent work that uses multimodal large language models for data augmentation in image, text, and speech modalities. It proposes an eight-step (image, text) or seven-step (speech) process view of LLM-based augmentation, organizes the literature into technique taxonomies (fifteen techniques per modality in the figures), tabulates 24 image, 45 text, and 35 speech papers, and discusses limitations and potential solutions for each modality. The central claim, stated in the abstract and in Section 1, is that this is the first survey to comprehensively cover LLM-based data augmentation across all three modalities.

Significance. If the survey's corpus and taxonomies were reliable, the paper would be a useful entry point for researchers wanting a cross-modal view of LLM-based augmentation, and the organized lists of limitations and proposed solutions would have practical value. The paper does gather a substantial number of relevant recent works, includes a dedicated discussion of 3D point-cloud augmentation, and provides a public GitHub repository. However, the survey's central claims of comprehensiveness and firstness are not supported by the evidence in the manuscript: the screening protocol is non-reproducible, several tabulated entries are not data-augmentation methods under the paper's own definition, the historical speech section contains non-augmentation works, and the claimed fifteen-technique taxonomies are contradicted by the enumerated lists. These are load-bearing issues for a survey whose stated contribution is comprehensive coverage.

major comments (5)

[Section 2.4] The inclusion criteria are effectively non-restrictive. The third eligibility question, 'Does the article propose a framework, tool, or methodology?', is satisfied by almost any paper, and the first two questions are redundant with the survey's topic. The screening is also described as consensus-based without any protocol for disagreements, and the search keywords, exact queries, date ranges, and per-database results are not reported. Without a reproducible search and screening protocol, the claim of comprehensive coverage in Section 1 and the abstract cannot be verified.
[Tables 1-3] Several tabulated papers do not perform data augmentation under the definition given in Section 1 (generating or modifying training samples for a downstream model). Table 1 lists DeepDR-LLM [106] and Med-MLLM [107], which are diagnostic and representation-learning systems; Table 2 lists OphGLM [176], an ophthalmology assistant, and Forged-GAN-BERT [175], an authorship-attribution method; Table 3 lists LLM-Commentator [212], LAMB [214], LaMini-Flan-T5 [215], and MMed-Llama 3 [217], which are commentary generation, LMS assistant, video summarization, and multilingual medical corpus efforts. These entries need to be removed or accompanied by an explicit explanation of the augmentation mechanism they propose; otherwise the 24/45/35 corpus counts and the fifteen-technique taxonomies are built on out-of-scope work.
[Section 3.2] The historical speech augmentation discussion includes works that are not data augmentation. Brandenburg et al. [66] is a surgical vocal-cord augmentation procedure, Watanabe et al. [67] is a teleconferencing system, Schmandt et al. [68] adds speech input to window systems, and Adams and Lang [69] studies the Lombard effect in Parkinson's patients. Presenting these as traditional speech data augmentation methods indicates that the screening criteria were not applied consistently and further weakens the internal consistency of the survey's scope.
[Sections 4.2.2 and 4.3.2] Both the text and speech subsections state that Figure 6 and Figure 7 outline 'fifteen diverse techniques,' but the enumerated lists contain only ten techniques in each case. The text subsection lists Paraphrasing, Back-Translation, Text Expansion, Role Playing, Synonym Replacement, Text Simplification, Textual Entailment Generation, Noise Injection, Contextual Variation, and Controlled Generation; the speech subsection lists ten similarly. The taxonomy counts are therefore internally inconsistent with the text, and the 'fifteen' number appears to be carried over from the image section without verification.
[Section 1 (Key Contributions)] The 'first comprehensive survey' claim is not substantiated. The manuscript does not compare its coverage with existing LLM-augmentation surveys such as Ding et al. [19], does not report a completeness analysis, and the non-reproducible protocol and out-of-scope entries described above prevent the reader from assessing whether the corpus is representative. The claim should be softened or supported by a systematic, reproducible selection process and a comparison with prior surveys.

minor comments (5)

[General] The manuscript contains numerous typographical errors, including 'augmmentation,' 'Challanges,' 'Amplititude,' 'uch as,' 'random swapm,' 'outweight,' and 'theroy'; the paper needs a careful proofreading pass.
[Section 2.1] The database is referred to as 'DBSL' and 'DataBase systems and Logic Programming platform,' but the link points to dblp.uni-trier.de; the correct name is DBLP (Digital Bibliography and Library Project).
[Figure 2] The figure caption says keywords are color-coded in red, green, and blue, but the figure appears to be in grayscale or with colors that are not clearly distinguishable; the color coding should be made accessible or replaced with labels.
[Section 4.1.1] The phrase 'the study by [106]' and similar citations are used informally; several citations in the text refer to bracketed numbers that are not consistently tied to the Tables, making it hard to trace which paper supports which step.
[Section 5.2] The discussion of DeepSeek R1 and reinforcement-learning-based self-augmentation is speculative and is not part of the reviewed corpus; it should be clearly labeled as a future outlook rather than a surveyed method.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found: the survey's taxonomy is assembled from external literature, and author self-citations appear only as illustrative examples, not as load-bearing premises.

full rationale

This is a literature survey rather than a derivation or empirical fitting paper, so the classic circularity failure modes (self-definitional predictions, fitted inputs renamed as predictions, uniqueness imported via self-citation, ansatz smuggled in via citation) do not apply. The paper's central claim—that it is the first comprehensive survey of LLM-based augmentation across image, text, and speech—rests on its own literature search and screening process (Sections 2.1–2.5), not on any prior result by the same authors. The self-citations present (e.g., DALL-E apple detection entries in Table 1 and the transparency work cited in Section 4.2.3) are used as examples of augmentation applications or as supporting references for limitations; none of them is used to define the taxonomy, to prove the firstness claim, or to forbid alternative frameworks. The broad inclusion criterion in Section 2.4 could admit papers outside the stated scope, and several Table entries (e.g., OphGLM, LLM-Commentator, LAMB) may not be data-augmentation methods; however, that is a correctness and scoping issue about corpus validity, not circularity, because the survey's conclusions are not forced by fitting parameters to its own outputs. No equation or definitional chain reduces a prediction to an input. Therefore the circularity burden is minimal.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The survey has no free parameters and no invented entities. Its central claims rest on two domain assumptions: the completeness and representativeness of the manually curated literature, and a broad interpretation of what counts as a multimodal LLM. Both assumptions are load-bearing for the claimed comprehensiveness and firstness.

assumptions (2)

domain assumption The selected literature of 24 image, 45 text, and 35 speech studies is representative of the full body of LLM-based data augmentation research.
Section 2.5 reports these counts, but the search queries, date ranges, and screening decisions are not itemized. The taxonomy and gap analysis depend on this coverage being complete.
domain assumption The category 'multimodal LLM' includes text-to-image generators such as DALL-E, diffusion models, and GANs, even though these are not all language models.
Section 4.1.2 lists DF-GAN, diffusion-based DALDA, and Point-E and Shap-E as LLM-based augmentation methods without a formal definition. The boundary of the survey topic is never explicitly drawn.

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Cite this review

Pith. "Pith review of Multimodal Large Language Models for Image, Text, and Speech Data Augmentation: A Survey." pith.science (2026). https://pith.science/paper/Y3GA5QWL

@misc{pith2026250118648,
  author       = {Pith},
  title        = {Pith review of: Multimodal Large Language Models for Image, Text, and Speech Data Augmentation: A Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y3GA5QWL}},
  note         = {Machine review of arXiv:2501.18648}
}

read the original abstract

In the past five years, research has shifted from traditional Machine Learning (ML) and Deep Learning (DL) approaches to leveraging Large Language Models (LLMs) , including multimodality, for data augmentation to enhance generalization, and combat overfitting in training deep convolutional neural networks. However, while existing surveys predominantly focus on ML and DL techniques or limited modalities (text or images), a gap remains in addressing the latest advancements and multi-modal applications of LLM-based methods. This survey fills that gap by exploring recent literature utilizing multimodal LLMs to augment image, text, and audio data, offering a comprehensive understanding of these processes. We outlined various methods employed in the LLM-based image, text and speech augmentation, and discussed the limitations identified in current approaches. Additionally, we identified potential solutions to these limitations from the literature to enhance the efficacy of data augmentation practices using multimodal LLMs. This survey serves as a foundation for future research, aiming to refine and expand the use of multimodal LLMs in enhancing dataset quality and diversity for deep learning applications. (Surveyed Paper GitHub Repo: https://github.com/WSUAgRobotics/data-aug-multi-modal-llm. Keywords: LLM data augmentation, Grok text data augmentation, DeepSeek image data augmentation, Grok speech data augmentation, GPT audio augmentation, voice augmentation, DeepSeek for data augmentation, DeepSeek R1 text data augmentation, DeepSeek R1 image augmentation, Image Augmentation using LLM, Text Augmentation using LLM, LLM data augmentation for deep learning applications)

Figures

Figures reproduced from arXiv: 2501.18648 by the authors.

**Figure 2.** Survey methodology and results overview: [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** A comprehensive overview of data augmentation techniques, divided into two main eras: 1990 to 2010, [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: LLM based image data augmentation : showing the technical aspect of how image augmentation [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]

**Figure 5.** Figure 5: Conceptual example of LLM-based 3D augmentation. Given a base object, a text prompt could specify [PITH_FULL_IMAGE:figures/full_fig_p018_5.png]

**Figure 6.** Figure 6: LLM based text data augmentation : showing the technical aspect of how text augmentation using [PITH_FULL_IMAGE:figures/full_fig_p020_6.png]

**Figure 7.** Figure 7: LLM based speech data augmentation : showing the technical aspect of how audio/speech augmenta [PITH_FULL_IMAGE:figures/full_fig_p028_7.png]

**Figure 8.** Figure 8: A mind map illustrating future perspectives of LLM-based data augmentation for image, text, and [PITH_FULL_IMAGE:figures/full_fig_p037_8.png]

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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

Breaking the Barriers of Text-Hungry and Audio-Deficient AI
cs.SD 2025-06 reject novelty 4.0 of 10

A proposed audio-native translation framework called MAST with fractional diffusion is described, but no evidence is given that it produces working translations.

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