The article examines how Large Language Models can aid in creating Entity-Relationship diagrams from natural language requirements. It tests three LLMs with three prompting strategies—Zero-Shot, Chain of Thought, and Chain of Thought + Verifier—on scenarios of increasing complexity. Findings show that while LLMs perform adequately on simpler tasks, their reliability drops with more complex requirements, leading to inconsistencies, ambiguities, and constraint representation failures.
arXiv:2607. 24707v1 Announce Type: new Abstract: Entity-Relationship Diagrams (ERDs) are central to conceptual database design, yet they are typically available only as rendered images rather than machine-readable schemas, limiting AI-assisted database engineering.
arXiv:2607. 26220v1 Announce Type: cross Abstract: Context: Large Language Models (LLMs) offer natural-language flexibility for automated requirements elicitation but frequently generate structurally invalid requirements and logical inconsistencies, lacking formal correctness guarantees.
Context: Large Language Models (LLMs) offer natural-language flexibility for automated requirements elicitation but frequently generate structurally invalid requirements and logical inconsistencies, lacking formal correctness guarantees. Objectives: This study aims to eliminate logical inconsistencies and enforce structural conformance in LLM-generated requirements while quantifying the LLM's pre-validation decision uncertainty within a formal domain model.
arXiv:2606. 08477v1 Announce Type: new Abstract: Knowledge extraction from symbolic data often produces abstractions that are formally defined but not immediately interpretable by users.
arXiv:2511. 14967v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown great promise in generating structured diagrams from natural language descriptions, particularly Mermaid sequence diagrams for software engineering.
The paper introduces FlowGen, a system that automates the construction of use case flows using large language models (LLMs). FlowGen extracts semantic elements via an LLM-based Semantic Information Processing module, builds a Semantic Relational Graph encoded by an enhanced R-GAT for basic flow generation (BFGen), and adds branch point prediction (BPP) and branch-conditioned alternative flow generation (AFGen). Experiments on 13 public and 7 industrial datasets show FlowGen outperforms baselines across precision, recall, F1, and AUC metrics for all three components.
arXiv:2606. 30441v1 Announce Type: cross Abstract: A rigorous formalization of system requirements is a fundamental prerequisite for the verification of Multi-Agent Systems (MAS).
arXiv:2507.21790v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are becoming widely used to support various workflows across different disciplines, yet their potential in discr...
arXiv:2601. 21787v2 Announce Type: replace-cross Abstract: The creation of Business Process Model and Notation (BPMN) models is a complex and time-consuming task requiring both domain knowledge and proficiency in modeling conventions.
arXiv:2607. 17963v1 Announce Type: new Abstract: Ontology extension refers to the process of enriching an existing ontology in response to emerging requirements, making it more complete.
arXiv:2606. 03705v1 Announce Type: new Abstract: Knowledge Graphs (KGs) are widely used to mitigate the limitations of Large Language Models (LLMs), such as outdated knowledge and hallucinations.
arXiv:2604. 22207v2 Announce Type: replace-cross Abstract: Due to the textual and repetitive nature of many Requirements Engineering (RE) artefacts, Large Language Models (LLMs) have proven useful to automate their generation and processing.