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About this paper
Dhia Elhaq Rzig, Dhruba Jyoti Paul, Kaiser Pister, Jordan Henkel, Foyzul Hassan
Researchers from the University of Michigan-Dearborn, University of Wisconsin-Madison, and Microsoft developed PromptDoctor, an empirically-grounded tool for automatically linting and repairing Developer Prompts embedded within source code. The tool detects and remediates bias, injection vulnerabilities, and sub-optimal performance, successfully de-biasing 68.29% of biased prompts, hardening 41.81% of vulnerable prompts, and improving optimization scores by up to 75.5% on various tasks.
Key insights
- A significant percentage of Dev Prompts in open-source software exhibit bias (3.46%) and injection vulnerabilities (10.75%), highlighting a critical need for automated detection and remediation.
- LLM-powered, generation-evaluation based prompt rewriting is an effective strategy for mitigating bias and hardening against injection attacks directly within Dev Prompts, without requiring costly LLM fine-tuning.
- Automatic prompt optimization can substantially improve performance across various grounded tasks (e.g., summarization, translation, error correction) and open-ended Q&A, demonstrating the value of iterative refinement.
Summary by alphaXiv.
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