Changing only the prompter's age and gender in AI coding prompts produces statistically significant differences in generated website interface design, template content, and code structure across 800 generated websites and a 20-person user study.
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PEFT fine-tuning of Code Llama yields feedback on student Java bugs that students judge equal to ChatGPT and better than prompt engineering, using BLEU/ROUGE/BERTScore plus human ratings.
citing papers explorer
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Biased or Personalized? The Impact of Personal Information on AI-driven Development
Changing only the prompter's age and gender in AI coding prompts produces statistically significant differences in generated website interface design, template content, and code structure across 800 generated websites and a 20-person user study.
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Fine-Tuning Models for Automated Code Review Feedback
PEFT fine-tuning of Code Llama yields feedback on student Java bugs that students judge equal to ChatGPT and better than prompt engineering, using BLEU/ROUGE/BERTScore plus human ratings.