4 Matching Annotations
  1. Last 7 days
    1. It has a panel of critics who tear my work apart from different angles—skills I wrote to invoke certain kinds of feedback, whether it's for length, pacing, or the soundness of the argument.

      大多数人认为AI写作缺乏批判性视角和严格编辑,但作者展示了一个由AI驱动的批评者团队,专门从不同角度撕碎她的作品。这挑战了人们对AI写作质量的担忧,表明AI可以被训练提供比传统编辑更全面、更严格的反馈,甚至可能超越人类编辑的一致性和广度。

  2. Jun 2024
  3. Jul 2021
    1. Recommendations DON'T use shifted PPMI with SVD. DON'T use SVD "correctly", i.e. without eigenvector weighting (performance drops 15 points compared to with eigenvalue weighting with (p = 0.5)). DO use PPMI and SVD with short contexts (window size of (2)). DO use many negative samples with SGNS. DO always use context distribution smoothing (raise unigram distribution to the power of (lpha = 0.75)) for all methods. DO use SGNS as a baseline (robust, fast and cheap to train). DO try adding context vectors in SGNS and GloVe.