引导式测验 · 中等 等级

字节对编码测验

回顾 BPE 合并、起始符号、字节覆盖、压缩起源、排名编码、罕见单词片段、词汇算术和 UTF-8 解码。

相关引导路径字节对编码
问题 1 的 8

Which adjacent-symbol operation is repeated in common BPE vocabulary training?

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  1. Which adjacent-symbol operation is repeated in common BPE vocabulary training?
    • Deleting all low-frequency words from the language
    • Merging a most-frequent pair into a larger symbol
    • Sorting complete documents by title
    • Selecting pairs solely by their alphabetical spelling
  2. BPE 在开始训练时将文本视为什么？
    • 整个句子
    • 固定 8 个代币区块
    • 单个字符或字节
    • 预翻译短语
  3. In a byte-level BPE configuration covering all 256 base byte values, how can unfamiliar valid UTF-8 text be represented?
    • Through its constituent bytes when larger pieces are unavailable
    • By storing every possible complete word in the finite vocabulary
    • By requiring the input to be translated into English first
    • By assigning every unseen character one shared unknown ID
  4. 在 NLP 采用 BPE 算法之前，它最初来自哪里？
    • 图像压缩
    • 音频编码
    • 数据库索引
    • 数据压缩
  5. When GPT-2's published BPE encoder finds multiple eligible adjacent pairs, what determines its merge choice?
    • A new random vocabulary fit on that one input
    • The text file's creation date
    • The priorities in the learned merge ranks
    • The order of a human-written list of target meanings
  6. What can happen when an unfamiliar word lacks larger learned BPE pieces?
    • It must be replaced by an unrelated synonym
    • It remains represented by several smaller available pieces
    • It necessarily becomes one new learned token during inference
    • It is always silently removed from the input
  7. An illustrative BPE vocabulary has 256 base byte tokens, two distinct added merge tokens, and one separate special token. What is its total size?
    • 256
    • 258
    • 512
    • 259
  8. In GPT-2's published decoder, what is reconstructed before converting the result from UTF-8 into text?
    • The complete byte sequence represented by the token pieces
    • An independent Unicode character from every individual token first
    • A new corpus-frequency table for training merges
    • The model's output-label probabilities