Fonksiyon Vektörleri ve Görev Gösterimleri Testi

Testi çözdüğünüzde cevaplar ve açıklamalar görünür.

1. What does a function vector represent in the studied language models?
   • A compact activation-space representation associated with a demonstrated task
   • The model’s full weight matrix
   • A single token embedding in every setting
   • The optimizer’s learning rate
2. Bir fonksiyon vektörü tipik olarak nasıl oluşturulur?
   • Kelime hazinesindeki tüm kelime yerleştirmelerini toplayarak
   • Örnek yönlendirmeler üzerinden belirli görev taşıma dikkatlerinin çıktılarının ortalamasını alarak
   • Modele bir örnek üzerinde ince ayar yaparak
   • Son softmax olasılıklarını okuyarak
3. What did the function-vector paper observe after injecting a vector into a zero-shot prompt?
   • The model always crashed
   • It guaranteed correct answers on any task
   • It could trigger the associated task in the tested settings
   • It permanently changed the model weights
4. Bir fonksiyon vektörü taşıyan başları bulmanın temelini oluşturan analiz tekniği hangisidir?
   • Kayıpta gradyan iniş
   • Işın arama
   • Bırakma düzenlemesi
   • Nedensel aracılık analizi
5. What do function-vector experiments provide evidence for?
   • Some models form reusable internal representations associated with tasks
   • All language models only memorize text
   • Every model has one universal task vector
   • Tokenization alone determines every behavior
6. Why are function vectors interesting to steering research?
   • They guarantee safe or correct behavior
   • They provide an activation-level intervention that can influence task behavior in tested settings
   • They eliminate prompts for every application
   • They make all model decisions fully transparent
7. At what part of the transformer did the paper report strong function-vector effects?
   • Only the input embedding table
   • Only the final output logits
   • Middle layers in the tested models
   • Tokenizer sözlüğü
8. What happened when researchers summed some function vectors?
   • The vectors always canceled to zero
   • The model’s weights were permanently updated
   • Every possible task was composed perfectly
   • Some combinations triggered new complex tasks in the tested experiments