Vichwa vya Utangulizi katika Maswali ya Transfoma

Majibu na maelezo huonekana unapojibu maswali.

1. Je, kichwa cha induction kimsingi hutekeleza sheria gani?
   • Tabiri ishara ya mara kwa mara katika lugha
   • Nakili tokeni iliyotangulia bila kubadilika kila wakati
   • Changanya tokeni za muktadha bila mpangilio
   • Tazama [A][B] mapema, kisha kwenye [A] tabiri tena [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. Je, 'kichwa cha ishara-uliopita' kina jukumu gani katika mzunguko wa utangulizi?
   • Inazalisha softmax ya mwisho
   • Inaondoa maelezo ya msimamo
   • Hunakili maelezo ya mtangulizi wa kila tokeni mbele ili ulinganishaji ufanyike
   • Inarekebisha uzito wa umakini
4. Je, ni vichwa vingapi vya usikivu vinavyohusika kwa kawaida katika mzunguko wa uingizaji wa kanuni?
   • Moja kabisa
   • Mbili, imeundwa kwa tabaka
   • Mamia katika safu moja
   • Sifuri; ni feed-forward tu
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. Ni kazi gani ambayo kichwa cha utangulizi kinaweza kushughulikia moja kwa moja?
   • Kuhesabu upinde rangi wa nambari
   • Kukandamiza uzani wa mfano
   • Inasimba kidokezo cha ingizo
   • Kurudia mlolongo kwa kunakili kilichofuata tokeni za awali zinazolingana
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