Ketua Induksi dalam Kuiz Transformers

Jawapan dan penjelasan muncul semasa anda mengambil kuiz.

1. Apakah peraturan yang secara asasnya dilaksanakan oleh ketua induksi?
   • Ramalkan token yang paling kerap dalam bahasa tersebut
   • Sentiasa salin token sebelumnya tidak berubah
   • Kocok token konteks secara rawak
   • Lihat [A][B] lebih awal, kemudian pada [A] sekali lagi ramalkan [B]
2. What capability was linked to induction-head behavior in the cited 2022 study?
   • In-context learning in the examined models, with broader large-model claims framed as hypotheses
   • A proof that every form of reasoning uses only one identical circuit
   • Changing the tokenizer vocabulary during every prompt
   • Permanent weight updates whenever a repeated token appears
3. Apakah peranan yang dimainkan oleh 'kepala token sebelumnya' dalam litar aruhan?
   • Ia menjana softmax akhir
   • Ia mengalih keluar maklumat kedudukan
   • Ia menyalin maklumat pendahulu setiap token ke hadapan supaya pemadanan boleh berlaku
   • Ia menormalkan berat perhatian
4. Berapakah bilangan kepala perhatian yang biasanya terlibat dalam litar aruhan berkanun?
   • tepat satu
   • Dua, terdiri merentasi lapisan
   • Beratus-ratus dalam satu lapisan
   • Sifar; ia adalah suapan ke hadapan sahaja
5. What training relationship did the induction-head study report in the models it examined?
   • Head formation coincided with changes in loss behavior and increased in-context learning
   • All heads were fully formed before any training
   • Induction behavior could only appear after every parameter was frozen forever
   • The same exact training transition must occur in every future model
6. Tugas yang manakah akan dikendalikan secara langsung oleh ketua induksi?
   • Mengira kecerunan berangka
   • Memampatkan berat model
   • Menyulitkan gesaan input
   • Mengulangi urutan dengan menyalin apa yang mengikuti token padanan sebelumnya
7. What roles do query-key matching and the output-value path play in the canonical induction circuit?
   • Query-key computations generate the final text filename; output-value computations train the tokenizer
   • Query-key computations select the matching context position; output-value computations promote the token information there
   • Both paths only count the total number of context words
   • The output-value path selects a position without any attention pattern
8. Does a demonstrated two-head induction circuit prove that every large-model in-context task uses that mechanism alone?
   • Yes; every architecture and prompt has already been causally tested
   • Yes; no other network computation can influence a prediction
   • No; the wider explanation remains a hypothesis needing model- and task-specific evidence
   • Only the number of parameters determines whether the proof is universal