Kuiz Penyahkodan Ketekalan Diri

Jawapan dan penjelasan muncul semasa anda mengambil kuiz.

1. How does self-consistency decoding pick a final answer from a language model?
   • Contohi banyak laluan penaakulan dan ambil undian majoriti pada jawapannya
   • Sentiasa pilih token tunggal yang berkemungkinan besar pada setiap langkah
   • Latih model pada outputnya sendiri berulang kali
   • Buang semua rawak daripada generasi
2. Under what condition can self-consistency improve answer accuracy?
   • Whenever the number of samples increases
   • When correct sampled paths converge and errors are not too strongly correlated
   • When every reasoning chain has identical wording
   • Only when the model weights are retrained
3. Apakah kaedah penyahkodan yang digantikan oleh ketekalan diri?
   • Penyebaran balik
   • Tokenisasi
   • Penyahkodan rakus satu rantai
   • Pengkuantitian berat
4. Bahagian manakah bagi setiap rantai sampel yang sebenarnya digunakan dalam undian akhir?
   • Teks penaakulan penuh
   • Bilangan token yang dijana
   • Tetapan suhu
   • Hanya jawapan muktamad
5. What is the main cost of increasing the number of self-consistency samples?
   • More inference compute; accuracy gains depend on the task and can have diminishing returns
   • It retrains the model weights after every response
   • It guarantees that the majority is correct
   • It eliminates the need to sample any reasoning paths
6. Ketekalan diri adalah contoh awal konsep yang lebih luas?
   • Pemampatan model
   • Penskalaan masa inferens (mengeluarkan pengiraan tambahan untuk menaakul lebih baik)
   • Penambahan data semasa latihan
   • Pecutan perkakasan
7. Why may answer normalization be useful before a self-consistency vote?
   • It makes all reasoning paths independent
   • It verifies the answer against external evidence
   • It can group semantically equivalent answers that use different formatting
   • It changes the model’s sampled probabilities
8. What is a key limitation of a majority answer from self-consistency?
   • A majority vote cannot be computed for text
   • Sampling always changes the model weights
   • Every reasoning path is guaranteed independent
   • Correlated reasoning errors can make an incorrect answer win the vote