LLM pipeline with generation-critic feedback reaches 61% accuracy on low-level goal extraction from requirements documents and outperforms standalone few-shot prompting, yet remains best suited as an accelerator for manual work.
R.Generating domain models from natural language text using nlp: a benchmark dataset and experimental comparison of tools.Software and Systems Modeling 23, 6 (2024), 1493–1511
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Evaluating LLM-Based Goal Extraction in Requirements Engineering: Prompting Strategies and Their Limitations
LLM pipeline with generation-critic feedback reaches 61% accuracy on low-level goal extraction from requirements documents and outperforms standalone few-shot prompting, yet remains best suited as an accelerator for manual work.