سیاق و سباق کی توسیع کوئز کے لیے پوزیشن انٹرپولیشن

جب آپ کوئز دیتے ہیں تو جوابات اور وضاحتیں ظاہر ہوتی ہیں۔

1. پوزیشن انٹرپولیشن کا بنیادی خیال کیا ہے؟
   • مزید توجہ دینے والے سر شامل کریں۔
   • پوزیشن کے اشاریہ جات کو ری اسکیل (سکڑنا) کریں تاکہ وہ ایکسٹرا پولٹنگ کے بجائے تربیت یافتہ رینج کے اندر رہیں
   • پوزیشنی ایمبیڈنگ کو مکمل طور پر حذف کریں۔
   • طویل ترتیب پر ماڈل کو شروع سے دوبارہ تربیت دیں۔
2. Which positional scheme did the original Position Interpolation work extend through position-index rescaling?
   • A fixed output-vocabulary lookup table only
   • No positional information of any kind
   • Rotary positional embeddings, or RoPE
   • An image compression format rather than model positions
3. Why can unmodified positional extrapolation degrade a RoPE model on much longer inputs?
   • The sequence introduces positional and attention conditions beyond the model's training experience
   • A longer context automatically retrains every parameter
   • RoPE removes all attention from the model
   • The tokenizer necessarily loses its vocabulary at a specific length
4. اگر سیاق و سباق کی طوالت کو L سے L' تک بڑھایا جائے، تو بنیادی PI کا اطلاق m پوزیشن پر کیا ہوتا ہے؟
   • m کو L'/L سے ضرب دیں۔
   • L' کو m میں شامل کریں۔
   • m کو صفر پر سیٹ کریں۔
   • m کو L'/L سے تقسیم کریں (یعنی، m / (L'/L))
5. How does YaRN's frequency-aware approach differ from uniform Position Interpolation?
   • It removes every query and key from attention
   • It changes only the output vocabulary size
   • It treats frequency bands differently to preserve local detail while extending positional range
   • It guarantees that all long-context tasks have identical accuracy
6. What makes the original Position Interpolation workflow different from pretraining a new long-context model from scratch?
   • It eliminates every need for computation
   • It adapts an existing pretrained model toward the tested longer window with rescaling and fine-tuning
   • It requires replacing all learned parameters with random values
   • It proves all models can extend indefinitely without evaluation
7. Does basic Position Interpolation inherently add new attention heads or enlarge the trainable architecture?
   • Yes; every doubled context requires twice as many attention heads
   • Yes; the vocabulary must expand by the context-length factor
   • Only the model's filename changes, with no effect on positions
   • No; it changes the position coordinates used by the existing model
8. Why must a longer-context PI deployment still assess memory and computation requirements?
   • It processes more tokens and stores relevant state; compressing position coordinates does not remove that work
   • The number of actual input tokens shrinks automatically with their positions
   • All KV-cache storage becomes zero after rescaling
   • Every extended model has unchanged latency regardless of input length