Kuiz Penyematan Kedudukan Putar

Jawapan dan penjelasan muncul semasa anda mengambil kuiz.

1. Bagaimanakah RoPE menyuntik maklumat kedudukan ke dalam pengubah?
   • Dengan memutarkan pasangan dimensi dalam pertanyaan dan vektor utama dengan sudut bergantung pada kedudukan
   • Dengan menambahkan vektor tetap pada benam input
   • Dengan menyusun semula token secara rawak
   • Dengan menambahkan nombor kedudukan sebagai teks
2. Selepas RoPE digunakan, skor perhatian antara dua token bergantung terutamanya pada:
   • Jawatan mutlak mereka sahaja
   • Jarak relatif mereka (m tolak n)
   • Saiz perbendaharaan kata
   • Saiz batch
3. What training-efficiency comparison did the YaRN paper report for context extension?
   • Exactly the same data and steps as all prior methods
   • No fine-tuning or training data of any kind
   • About 10x fewer tokens and 2.5x fewer training steps than prior methods
   • A fixed 100x increase in output tokens
4. Mengapa pasangan dimensi yang berbeza berputar pada frekuensi yang berbeza dalam RoPE?
   • Untuk menjimatkan memori dengan melangkau beberapa dimensi
   • Untuk rawak keluaran model
   • Kerana semua pasangan mesti menggunakan sudut yang sama
   • Untuk menangkap kedua-dua susunan tempatan yang baik dan kedudukan jarak jauh
5. What is the goal of techniques such as Position Interpolation and YaRN for RoPE-based models?
   • Extend the usable context length beyond the original training window using additional scaling/fine-tuning methods
   • Guarantee accurate recall at any sequence length with no evaluation
   • Remove position information from attention
   • Replace all token embeddings with absolute learned vectors
6. Does standard RoPE require learned absolute-position vectors to inject position into query and key representations?
   • Yes; it adds one learned vector for every possible sequence position
   • No; it applies position-dependent rotations to paired query/key dimensions
   • It removes query and key vectors entirely
   • It only adds position to value vectors
7. What does standard RoPE alone guarantee about a model’s context length?
   • Unlimited accurate context at every position
   • A fixed context of exactly 128 tokens
   • It does not by itself guarantee reliable use beyond the trained context length
   • Removal of the attention mechanism
8. What extended context length did the Position Interpolation paper report for its LLaMA evaluations?
   • Exactly 1,000 tokens with no position changes
   • Unlimited context with no fine-tuning
   • Up to 2 million tokens for all models
   • Up to 32,768 tokens with minimal fine-tuning