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Showing 1–3 of 3 results
Advanced filters: Author: Lingjuan Lyu Clear advanced filters
  • Interest in using large language models such as ChatGPT has grown rapidly, but concerns about safe and responsible use have emerged, in part because adversarial prompts can bypass existing safeguards with so-called jailbreak attacks. Wu et al. build a dataset of various types of jailbreak attack prompt and demonstrate a simple but effective technique to counter these attacks by encapsulating users’ prompts in another standard prompt that reminds ChatGPT to respond responsibly.

    • Yueqi Xie
    • Jingwei Yi
    • Fangzhao Wu
    Research
    Nature Machine Intelligence
    Volume: 5, P: 1486-1496
  • Mainstream personalization methods rely on centralized Graph Neural Network learning on global graphs, which have considerable privacy risks due to the privacy-sensitive nature of user data. Here, the authors present a federated GNN framework for both effective and privacy-preserving personalization.

    • Chuhan Wu
    • Fangzhao Wu
    • Xing Xie
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-10
  • This work presents a communication-efficient federated learning method that saves a major fraction of communication cost. It reveals the advantage of reciprocal learning in machine knowledge transfer and the evolutional low-rank properties of deep model updates.

    • Chuhan Wu
    • Fangzhao Wu
    • Xing Xie
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-8