Maswali ya Ufasiri wa Kimechanical

Majibu na maelezo huonekana unapojibu maswali.

1. Je, lengo kuu la ufasiri wa kiufundi ni lipi?
   • Ili kubadilisha uhandisi hesabu za ndani za mtandao kuwa algoriti zinazoeleweka na binadamu
   • Kufanya mifano ya mafunzo kwa kasi
   • Ili kubana mifano ya vifaa vya rununu
   • Ili kupanga vipengele vya ingizo kwa umuhimu pekee
2. 'Superposition' inarejelea nini kwenye mtandao wa neva?
   • Kuweka mifano mingi pamoja
   • Inawakilisha vipengele vingi kuliko vipimo kwa kutumia maelekezo yanayopishana
   • Kuendesha muundo kwenye GPU nyingi
   • Utabiri wa wastani kutoka kwa ukimbiaji tofauti
3. Visimbaji kiotomatiki vya Sparse hutumika katika ukalimani wa kiufundi kimsingi kwa...
   • Tengeneza data ya mafunzo
   • Kuongeza kasi ya inference
   • Tenganisha uanzishaji wa polisemantiki kuwa vipengele safi, vinavyoweza kufasirika zaidi
   • Simba uzani wa mfano
4. 'Vichwa vya induction' ni nini?
   • Viongeza kasi vya vifaa vya transfoma
   • Safu za pato zinazozalisha maandishi
   • Aina ya kazi ya kupoteza
   • Vichwa vya tahadhari ambavyo vinakili na kuendelea na mifumo ya tokeni inayorudiwa, inayosaidia ujifunzaji wa ndani ya muktadha
5. Watafiti wanathibitishaje kuwa mzunguko uliogunduliwa kweli hufanya hesabu ya kudhaniwa?
   • Kupitia uingiliaji kati wa sababu kama vile kuzima au kuweka viraka kuwezesha uanzishaji maalum
   • Kwa kuangalia usahihi wa mafunzo ya mfano
   • Kwa kuhesabu idadi ya vigezo
   • Kwa kupima latency ya makisio
6. What is a cautious safety-related reason to study mechanistic interpretability?
   • It proves every hidden behavior can already be found
   • It may provide evidence about internal computations, while current methods are not a complete audit or direct steering guarantee
   • It guarantees a model will follow any requested correction
   • It replaces evaluations of model behavior in deployment
7. If a sparse autoencoder feature activates on examples described as “Golden Gate Bridge,” what does that observation establish?
   • The entire model’s computation is now explained
   • The feature is guaranteed to be a single neuron
   • A candidate feature correlates with those examples and needs further testing to establish its role
   • The feature causes every bridge-related output
8. Why test a candidate feature across varied prompts and examples?
   • Proof that the model has no other relevant circuits
   • A guarantee that the explanation generalizes to every prompt
   • A replacement for inspecting model behavior
   • To check whether the observed activation pattern generalizes beyond selected examples