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Perplexity 和语言指标测验

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相关引导路径Perplexity 和语言指标
问题 1 的 8

Under the same autoregressive text-scoring setup, what does lower perplexity indicate?

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  1. Under the same autoregressive text-scoring setup, what does lower perplexity indicate?
    • Lower average negative log-likelihood for the reference tokens
    • A guarantee of more factually correct generated answers
    • A guarantee of a larger parameter count
    • A requirement that every top-choice prediction be correct
  2. Using natural-log negative log-likelihood, how is autoregressive perplexity computed from its average?
    • By multiplying the parameter count by the context length
    • By subtracting the average from the number of attention heads
    • By taking the average learning rate
    • By exponentiating the average negative log-likelihood
  3. 模型在不进行学习的情况下为 10,000 个单词的词汇分配统一的概率。它的困惑是什么？
    • 1
    • 10,000
    • 0
    • 100
  4. Why do tokenizer and corpus settings matter when directly comparing per-token perplexity values?
    • Perplexity ignores every scored reference token
    • All corpora necessarily produce the same likelihood
    • They change the units or reference data over which probabilities are averaged
    • Only model parameter count determines the score
  5. Which translation-reference metric uses modified n-gram precision and a brevity penalty?
    • Perplexity
    • 均方像素误差
    • 模型参数个数
    • 蓝线
  6. 是什么关键限制让研究人员摆脱了判断现实世界质量的困惑？
    • Perplexity 测量下一个单词的预测，而不是输出是否有用、真实或有帮助
    • Perplexity 的计算成本太高
    • Perplexity 无法对文本数据进行计算
    • Perplexity 仅在推理期间有效，在训练期间无效
  7. When scoring a long text with a fixed-context model, what can a sliding-window procedure provide that disjoint chunks lose near boundaries?
    • A new vocabulary learned automatically at every boundary
    • A guarantee that generated answers are factually correct
    • More preceding context for the scored target tokens
    • A requirement to count each overlapping target repeatedly
  8. Why is conventional left-to-right perplexity not directly the same score for a BERT-style masked language model?
    • BERT has no learned parameters
    • The masked model does not directly define the same autoregressive sequence factorization
    • Every masked-token probability is necessarily zero
    • Perplexity is defined only for image pixels