Idanwo Itumọ Mechanistic

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. Kini ibi-afẹde aringbungbun ti itumọ mechanistic?
   • Lati yi pada-ẹrọ ẹrọ awọn iṣiro inu nẹtiwọọki sinu awọn algoridimu ti eniyan ni oye
   • Lati jẹ ki awọn awoṣe ṣe ikẹkọ yiyara
   • Lati compress awọn awoṣe fun awọn ẹrọ alagbeka
   • Lati ṣe ipo awọn ẹya igbewọle nipasẹ pataki nikan
2. Kini 'superposition' tọka si ni nẹtiwọọki nkankikan?
   • Stacking ọpọlọpọ awọn awoṣe jọ
   • Aṣoju awọn ẹya diẹ sii ju awọn iwọn wa lọ nipasẹ lilo awọn itọnisọna agbekọja
   • Nṣiṣẹ awoṣe lori ọpọ GPUs
   • Awọn asọtẹlẹ aropin lati oriṣiriṣi awọn ṣiṣe
3. Awọn adaṣe autoencoders fọnka ni a lo ni itumọ mechanistic nipataki lati…
   • Ṣẹda data ikẹkọ
   • Iyara soke itọkasi
   • Disentangle polysemantic awọn iṣẹ ṣiṣe sinu mimọ, awọn ẹya itumọ diẹ sii
   • Encrypt awoṣe òṣuwọn
4. Kini awọn 'ori induction'?
   • Hardware accelerators fun Ayirapada
   • Awọn ipele igbejade ti o ṣe agbejade ọrọ
   • A iru ti isonu iṣẹ
   • Awọn ori akiyesi ti o daakọ ati tẹsiwaju awọn ilana ami ami atunwi, ṣe atilẹyin ikẹkọ inu-ọrọ
5. Bawo ni awọn oniwadi ṣe jẹrisi pe iyika ti a ṣe awari lotitọ ṣe iṣiro iṣiro kan?
   • Nipasẹ awọn ilowosi idi bi ablating tabi patching awọn iṣẹ ṣiṣe kan pato
   • Nipa yiyewo awọn awoṣe ká ikẹkọ išedede
   • Nipa kika awọn nọmba ti paramita
   • Nipa wiwọn airotẹlẹ inference
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