Size matters less: how fine-tuned small LLMs excel in BPMN generation
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The generation of Business Process Model and Notation (BPMN) XML outputs from textual process descriptions presents a promising application for large language models (LLMs), yet it introduces significant challenges due to the structured and precise nature of process modeling. This study evaluates the performance of LLMs—Mistral, GPT-4o, Gemini 1.5 Pro, and Claude 3.5 Sonnet—in BPMN generation, employing prompt engineering strategies across Simple, Medium, and Complex process descriptions to establish a baseline. Our findings reveal key limitations in LLMs, including limited output control, input presentation dependencies, and a lack of explainability, particularly for complex processes with nested flows and intricate dependencies. To address these challenges, we propose a novel Description-to-DOT pipeline utilizing a fine-tuned Qwen2.5 14B Coder model, trained on the MaD dataset of process description-DOT representation pairs. The novelty of the Description-to-DOT pipeline lies in its use of Graphviz DOT format as an intermediate representation, which requires generating fewer tokens and enables faster completion, followed by a Python script that converts DOT to BPMN XML in milliseconds—a significant efficiency improvement over the direct Description-to-BPMN pipeline, with the Description-to-DOT pipeline being approximately 6 times faster for Medium processes and 11 times faster for Complex processes. Experimental results demonstrate that the fine-tuned model significantly outperforms the evaluated LLMs, achieving accurate BPMN generation across all complexity levels. This study contributes: (1) Identification of LLM limitations in BPMN generation, such as logical inconsistencies, (2) A novel Description-to-DOT pipeline enhancing efficiency and accuracy, (3) A new benchmark dataset from the MaD dataset for Description-to-BPMN tasks, and (4) Comprehensive validation of the approach across complexity levels. These findings demonstrate the transformative potential of fine-tuned SLMs, with the Qwen2.5 Coder 14B enabling a scalable Description-to-DOT pipeline that excels in BPMN automation across complexity levels, validated on the MaD dataset.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it