LLMs with two prompting strategies and model validation tools produce mostly syntactically correct, conforming, semantically realistic and diverse instances of UML class diagrams.
Large language models for software engi- neering: A systematic literature review
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Few-shot prompting with the 32B DeepSeek-R1 model achieves the highest accuracy on a balanced set of 3,200 conventional commits mined from InfluxDB, while chain-of-thought adds no benefit and larger model scale improves results.
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LLM-based Generation of Semantically Diverse and Realistic Domain Model Instances
LLMs with two prompting strategies and model validation tools produce mostly syntactically correct, conforming, semantically realistic and diverse instances of UML class diagrams.
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Conventional Commit Classification using Large Language Models and Prompt Engineering
Few-shot prompting with the 32B DeepSeek-R1 model achieves the highest accuracy on a balanced set of 3,200 conventional commits mined from InfluxDB, while chain-of-thought adds no benefit and larger model scale improves results.