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トランスフォーマークイズ

セルフ アテンション、位置情報、クエリ、キーと値、アーキテクチャのバリアント、因果マスク、コンテキストの制限、およびアテンションの重みの制限を確認します。

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質問 1 の 8

トランス アーキテクチャを紹介した 2017 年の論文はどれですか?

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  1. トランス アーキテクチャを紹介した 2017 年の論文はどれですか?
    • Sequence to Sequence Learning with Neural Networks
    • 必要なのは注意力だけです
    • Neural Machine Translation by Jointly Learning to Align and Translate
    • Convolutional Sequence to Sequence Learning
  2. 自己注意は変圧器に何をさせるのでしょうか？
    • Retrieve missing facts from an external dictionary
    • Process positions only after the previous position is complete
    • Combine information from other positions in the same sequence into representations
    • Remove the need for position information in every architecture
  3. Which comparison matches the guide’s description of transformer architecture variants?
    • The original was encoder-only; later versions all use recurrent decoders
    • The original used an encoder-decoder design; later models may be encoder-only, decoder-only, or encoder-decoder
    • The original was decoder-only; later versions use only convolutions
    • The original omitted attention; later versions introduced it
  4. なぜ変圧器に位置エンコーディングが必要なのでしょうか?
    • Because sequence order can matter
    • To map each token ID to its vocabulary embedding
    • To prevent future tokens from being used during next-token prediction
    • To set the maximum number of tokens a system accepts
  5. In attention, what are query, key, and value vectors used for?
    • Queries rank possible output tokens, while keys contain the values to copy
    • Values are compared with queries to create weights, and keys are summed
    • Queries encode positions, keys encode token identities, and values set the mask
    • Query-key compatibility determines weights used to combine value vectors
  6. What restriction does causal attention impose during next-token prediction?
    • It prevents a position from using earlier tokens
    • It lets each position attend only to the immediately previous token
    • It prevents a position from attending to future tokens
    • It makes each position attend only to itself
  7. What does an advertised context limit establish about a model’s performance on a long document?
    • That it will use every part of the document reliably
    • Only the amount of context the system can accept; task performance still needs testing
    • That it will answer every question about the document
    • That the model trained on documents of that length
  8. What do attention weights establish about why a model produced its final answer?
    • They are internal calculations, not automatically a faithful explanation
    • They reveal a complete causal account of the answer
    • They prove the answer is factually correct
    • They verify every source used in the response