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arxiv: 2604.10350 · v1 · submitted 2026-04-11 · 💻 cs.SE

LLM-based Generation of Semantically Diverse and Realistic Domain Model Instances

Andrei Coman , Lola Burgue\~no , Dominik Bork , Manuel Wimmer This is my paper

Pith reviewed 2026-05-10 15:21 UTC · model grok-4.3

classification 💻 cs.SE
keywords LLMdomain modelUML class diagraminstance generationsemantic realismmodel validationdiversityprompting strategies
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The pith

Large language models can generate mostly correct and semantically realistic instances of UML domain models when prompted with class diagram descriptions.

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

The paper shows how large language models can be prompted to create concrete instances of domain models from UML class diagrams, going beyond structural validity to include realistic values and semantic diversity. The method pairs two prompting strategies with standard model validation tools to check for syntactic correctness, conformance to the model, and semantic coherence within each generated instance. Experiments on educational and published models from multiple domains produced instances that were largely correct with only a few semantic errors and values that varied while staying true to the domain and internally consistent. If this holds, modelers gain a way to obtain human-understandable examples automatically instead of constructing them by hand. Such instances would then support teaching domain concepts and supplying varied data for research without violating the original model rules.

Core claim

LLMs prompted with class diagram descriptions, used together with existing validation tools, produce instances that are mostly syntactically correct, conform to the domain model, contain only a few semantic errors, and exhibit semantically diverse realistic values whose combinations within each instance remain coherent.

What carries the argument

The combination of large language models with two prompting strategies applied to class diagram descriptions, followed by validation with existing model-checking tools.

If this is right

  • Educators obtain ready-to-use concrete examples for teaching domain modeling without manual construction.
  • Research projects can draw on diverse yet model-conformant data sets for analysis or simulation.
  • Modeling environments can automate the creation of test populations that respect both structure and domain meaning.
  • The effort to prepare example instances for validation or demonstration drops while preserving semantic realism.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same prompting-plus-validation pattern might apply to other diagram types such as state machines or activity diagrams.
  • Adding domain-specific knowledge bases could further reduce the remaining semantic errors observed in the experiments.
  • Scaling tests to larger industrial models would show whether the approach remains practical when class counts and constraints grow.
  • Generated instances could serve as training data for other model-related machine learning tasks that need realistic examples.

Load-bearing premise

Large language models given only class diagram descriptions can reliably infer real-world domain semantics and produce coherent value sets without extra domain training or knowledge bases.

What would settle it

Running the generation process on a fresh collection of domain models and finding that most resulting instances contain multiple semantic inconsistencies or unrealistic value combinations.

Figures

Figures reproduced from arXiv: 2604.10350 by Andrei Coman, Dominik Bork, Lola Burgue\~no, Manuel Wimmer.

Figure 1
Figure 1. Figure 1: Class diagram example (top) with example instantiation [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the two instance generation strategies [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Two generated Bank domain model instances, the left one following the Instruction Learning approach, the right one [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Two generated Bank domain model instances, the left one following the Chain-of-Thought approach in the edge scenario, [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
read the original abstract

Large Language Models (LLMs) have been recently proposed for supporting domain modeling tasks mostly related to the completion of partial models by recommending additional model elements. However, there are many more modeling tasks, one of them being the instantiation of domain models to represent concrete domain objects. While there is considerable work supporting the generation of structurally valid instantiations, there are still open challenges to incorporating real-world semantics by having realistic values contained in instances and ensuring the generation of semantically diverse models. Only then will such generated models become human-understandable and helpful in educational or data-driven research contexts. To tackle these challenges, this paper presents an approach that employs LLMs and two prompting strategies in combination with existing model validation tools for instantiating semantically realistic and diverse domain models expressed as UML class diagrams. We have applied our approach to models used in education and available in the literature from different domains and evaluated the generated instances in terms of syntactic correctness, model conformance, semantic correctness, and diversity of the generated values. The results show that the generated instances are mostly syntactically correct, that they conform to the domain model, and that there are only a few semantic errors. Moreover, the generated instance values are semantically diverse, i.e., concrete realistic examples in line with the domain and the combination of the values within one model are semantically coherent.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes an LLM-based method using two prompting strategies combined with existing model validation tools to instantiate UML class diagrams. The generated instances are evaluated for syntactic correctness, conformance to the domain model, semantic correctness (few errors), and semantic diversity (realistic, domain-appropriate values with coherent combinations within each instance). The approach is tested on educational and literature models from multiple domains, with results indicating mostly positive outcomes on all four criteria.

Significance. If the semantic realism and diversity claims hold under rigorous evaluation, the work would address a clear gap in domain model instantiation: moving beyond structural validity to produce human-understandable, realistic examples useful for education and data-driven research. The combination of LLMs with validation tools is a practical contribution, but its impact depends on demonstrating that the semantic properties are reproducible and not artifacts of subjective assessment.

major comments (2)
  1. [Evaluation] Evaluation section: the abstract and results claim 'mostly syntactically correct,' 'conform to the domain model,' 'only a few semantic errors,' and 'semantically diverse' values that are 'concrete realistic examples' with 'semantically coherent' combinations, yet no exact metrics, trial counts, error definitions, or inter-rater reliability statistics are provided. This makes the central empirical claims difficult to reproduce or falsify.
  2. [Results] Results section: semantic correctness and diversity are assessed without reported baselines (e.g., random or template-based value assignment), control conditions, or comparison to prior non-LLM instantiation techniques. Without these, it is unclear whether the LLM prompting genuinely improves semantic realism or merely produces plausible output.
minor comments (2)
  1. [Approach] The description of the two prompting strategies would benefit from explicit examples of the prompts used and how they differ in handling class attributes versus associations.
  2. [Evaluation] Consider reporting the exact number of models tested, their sizes (number of classes/attributes), and the domains represented to allow readers to assess generalizability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments, which highlight important opportunities to strengthen the empirical rigor of our work. We address each major comment below and commit to revisions that improve reproducibility and contextualization without altering the core claims or contributions of the manuscript.

read point-by-point responses

  1. Referee: [Evaluation] Evaluation section: the abstract and results claim 'mostly syntactically correct,' 'conform to the domain model,' 'only a few semantic errors,' and 'semantically diverse' values that are 'concrete realistic examples' with 'semantically coherent' combinations, yet no exact metrics, trial counts, error definitions, or inter-rater reliability statistics are provided. This makes the central empirical claims difficult to reproduce or falsify.

    Authors: We agree that the current Evaluation section would benefit from greater quantitative detail and explicit definitions to support reproducibility. In the revised manuscript, we will expand this section to report the precise number of generation trials and instances produced for each domain model, exact counts and percentages for syntactic correctness and model conformance (e.g., number of instances passing parser checks and OCL validation), clear operational definitions of semantic errors (e.g., attribute values that are implausible given domain knowledge or combinations that violate real-world coherence), and the assessment procedure for semantic diversity (manual review of value variety and intra-instance coherence). The semantic evaluation was performed by the authors with iterative cross-checking for consensus; we will describe this process in detail and acknowledge the absence of formal inter-rater reliability metrics as a limitation. revision: yes

  2. Referee: [Results] Results section: semantic correctness and diversity are assessed without reported baselines (e.g., random or template-based value assignment), control conditions, or comparison to prior non-LLM instantiation techniques. Without these, it is unclear whether the LLM prompting genuinely improves semantic realism or merely produces plausible output.

    Authors: Our primary aim was to demonstrate the feasibility of the LLM-based approach combined with validation tools for producing instances with the targeted semantic properties, rather than to perform a full comparative benchmark. We acknowledge that the absence of explicit baselines leaves open questions about relative improvement. In the revision, we will add a dedicated subsection in Results that compares our outputs to prior non-LLM techniques discussed in the related work (e.g., random instantiation and constraint-based solvers), explaining that such methods reliably achieve structural validity but typically produce semantically unrealistic or repetitive values. We will also include a small illustrative baseline using template-based random assignment on one of the evaluated models to contrast semantic quality, while noting that a comprehensive controlled experiment lies beyond the scope of this feasibility study. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical evaluation is independent of inputs

full rationale

The paper presents a prompting-based LLM method for generating UML class diagram instances, then evaluates outputs via mechanical validation tools for syntactic correctness and model conformance plus separate checks for semantic correctness and value diversity. No equations, fitted parameters, predictions derived from those parameters, self-definitional constructs, uniqueness theorems, or ansatzes appear in the abstract or described approach. Central claims rest on external validation steps rather than reducing to the generation process by construction, so the derivation chain is self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms or invented entities are introduced in the abstract; the work relies on off-the-shelf LLMs and existing model validation tools.

pith-pipeline@v0.9.0 · 5540 in / 1063 out tokens · 53743 ms · 2026-05-10T15:21:12.681091+00:00 · methodology

discussion (0)

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