用推荐系统方法提升大模型提示词的伦理安全性。
Ethical AI prompt recommendations in large language models using collaborative filtering
- 基于用户交互数据，用协同过滤筛选伦理提示词。
- 构建了合成提示推荐数据集，支持伦理评估。
- 适合关注AI伦理、提示工程安全的研究者。
随着大型语言模型（LLMs）推动AI发展，确保提示词推荐的伦理合规性至关重要。尽管LLMs带来创新，但存在偏见、公平性问题及问责困境。传统监管方法难以规模化，亟需动态解决方案。本文提出利用推荐系统中的协同过滤技术，优化伦理提示词选择。通过分析用户交互数据，该方法能有效促进伦理规范，降低偏见。研究贡献包括构建一个用于提示推荐的合成数据集，并首次将协同过滤应用于伦理提示筛选。同时，论文应对了偏见缓解、透明度提升和防范不道德提示工程等挑战。
原文摘要 · Abstract (English)
As large language models (LLMs) shape AI development, ensuring ethical prompt recommendations is crucial. LLMs offer innovation but risk bias, fairness issues, and accountability concerns. Traditional oversight methods struggle with scalability, necessitating dynamic solutions. This paper proposes using collaborative filtering, a technique from recommendation systems, to enhance ethical prompt selection. By leveraging user interactions, it promotes ethical guidelines while reducing bias. Contributions include a synthetic dataset for prompt recommendations and the application of collaborative filtering. The work also tackles challenges in ethical AI, such as bias mitigation, transparency, and preventing unethical prompt engineering.
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