Logit Lens 및 중간층 디코딩 퀴즈

퀴즈를 풀면 정답과 설명이 나타납니다.

1. What readout does a plain logit lens reuse to obtain vocabulary scores from an intermediate state?
   • A required new image classifier for every layer
   • The model's appropriate final normalization and unembedding output projection
   • A random dictionary sort with no model parameters
   • A separate retrained tokenizer for each prompt
2. Why can the same output projection be applied mathematically at multiple residual-stream depths?
   • Every layer must contain an identical copy of the final state
   • The entire transformer is a single linear mapping
   • All intermediate token scores are guaranteed identical
   • The states have compatible hidden dimensions, although their representations may not be equally calibrated for that readout
3. 일반 로짓 렌즈에 비해 '튜닝 렌즈'는 어떤 문제를 해결합니까?
   • 학습된 아핀 변환으로 레이어별 불일치를 수정합니다.
   • 언베딩이 필요하지 않습니다.
   • 추론 시 모델의 속도가 빨라집니다.
   • Softmax를 argmax로 대체합니다.
4. If an intermediate lens readout favors a correct factual token, what has been directly observed?
   • The unique causal location of the model's factual knowledge
   • A guarantee that all later output will be correct
   • That the particular probe maps that state to a high score for the token
   • The exact reasoning process of a conscious system
5. 로짓 렌즈는 무엇에 가장 직접적으로 유용합니까?
   • 모델 가중치 압축
   • 언어 간 번역
   • 모델 활성화 암호화
   • 심도 전반에 걸쳐 예측이 어떻게, 어디서 형성되는지 관찰
6. Why can a plain final-head readout be misleading on early transformer states?
   • Early states always use a different vocabulary file
   • Their representation distributions can differ from the final state for which the head was trained
   • Every early-layer vector is necessarily unstructured noise
   • No matrix operation can be applied before the final layer
7. A lens uses a model with a fifty-thousand-entry vocabulary and a 768-dimensional hidden state. How many vocabulary logits does its unembedding produce?
   • 768, one score per hidden coordinate only
   • One, because the token must be chosen before projection
   • Fifty thousand, one score per vocabulary entry
   • Exactly the number of transformer layers
8. What does softmax add to a logit-lens vocabulary-score display?
   • A normalized distribution over the projected vocabulary scores
   • Proof that every displayed probability is a calibrated belief
   • A causal intervention on the model's hidden states
   • A replacement for the output projection's vocabulary dimension