Induction Heads in Transformers Kediska

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Xeerkee ayuu dhab ahaantii hirgeliyaa madaxa kicinta?
   • Saadaali calaamada ugu badan ee luqadda
   • Had iyo jeer nuqul ka samee calaamadihii hore oo aan isbeddelin
   • Si aan kala sooc lahayn u isku shaandheyn calaamadaha macnaha guud
   • Eeg [A] [B] hore, ka dibna [A] mar kale saadaali [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. Doorkee ayuu 'madaxa calaamada hore' ka ciyaara wareegga soo-gelinta?
   • Waxay abuurtaa softmax ugu dambeeya
   • Waxay meesha ka saartaa macluumaadka booska
   • Waxay koobiyaysaa calaamo kasta xogtii ka horaysay si ay isu dhigma u dhacdo
   • Waxay caadi ka dhigtaa miisaanka dareenka
4. Immisa madax ayaa sida caadiga ah ku lug leh wareegga soo jeedinta canonical?
   • Dhab ahaan hal
   • Laba, oo ka kooban lakabyo
   • Boqollaal hal lakab ah
   • Eber; waa feed-hore kaliya
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. Hawsha keebaa si toos ah u maamuli lahaa madaxa soo kicinta?
   • Xisaabinta jaangooyo tirooyin ah
   • Cadaadiska miisaanka modelka
   • Sirin gelinta degdegta
   • Ku celcelinta isku xigxiga adiga oo koobiyeynaya waxa raacay calaamadihii hore ee iswaafajinta
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