The Impact of Critique on LLM-Based Model Generation from Natural Language: The Case of Activity Diagrams (2509.03463v1)

Abstract: LLMs show strong potential for automating the generation of models from natural-language descriptions. A common approach is an iterative generate-critique-refine loop, where candidate models are produced, evaluated, and updated based on detected issues. This process needs to address: (1) structural correctness - compliance with well-formedness rules - and (2) semantic alignment - accurate reflection of the intended meaning in the source text. We present LADEX (LLM-based Activity Diagram Extractor), a pipeline for deriving activity diagrams from natural-language process descriptions using an LLM-driven critique-refine process. Structural checks in LADEX can be performed either algorithmically or by an LLM, while alignment checks are always performed by an LLM. We design five ablated variants of LADEX to study: (i) the impact of the critique-refine loop itself, (ii) the role of LLM-based semantic checks, and (iii) the comparative effectiveness of algorithmic versus LLM-based structural checks. To evaluate LADEX, we compare the generated activity diagrams with expert-created ground truths using trace-based operational semantics. This enables automated measurement of correctness and completeness. Experiments on two datasets indicate that: (1) the critique-refine loop improves structural validity, correctness, and completeness compared to single-pass generation; (2) algorithmic structural checks eliminate inconsistencies that LLM-based checks fail to detect, improving correctness by an average of 17.81% and completeness by 13.24% over LLM-only checks; and (3) combining algorithmic structural checks with LLM-based semantic checks, implemented using the reasoning-focused O4 Mini, achieves the best overall performance - yielding average correctness of up to 86.37% and average completeness of up to 88.56% - while requiring fewer than five LLM calls on average.