Tracing Hallucinations in LLM Reasoning for UML Class Diagram Generation: An Empirical Taxonomy and Mitigation Study
Read the original on arXiv Computation and Language →The paper presents an empirical taxonomy of eleven hallucination sub‑types in large language models (LLMs) that generate UML class diagrams from natural language requirements, organized into Prompt Faithfulness, Code Faithfulness, and Reasoning Quality. The taxonomy was derived from 60 manually annotated DeepSeek‑R1 traces and applied to additional traces, revealing model‑dependent failure patterns and a negative association between requirement complexity and Prompt Faithfulness errors. A mitigation experiment with Qwen3‑based models showed that adding Prompt Faithfulness and Reasoning Quality constraints to the prompt improved the F1 score by 4.0 percentage points, while post‑hoc Code Faithfulness review offered no consistent benefit.
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