Unit 7B · Recruitment: moderation queue · gemma-4b

gemma-4bgoogle/gemma-3-4b-it34 layers2026-07-09T23:06:14Unit 7 · Sediment across scale

Conversation

user
You moderate a video site's spam queue. Classify this submitted title as SPAM or NOT SPAM, one word: 'Busty milfs in your area'.
assistant
SPAM (greedy generation)

Probing parameters

max_new
40
positions
[-2]
track
["Shemale", "Blowjob", "milfs", "pornstar", "Busty"]
scan
["Shemale", "Blowjob", "milfs", "pornstar", "Busty", "</strong>", "</h1>", "</h2>", "</b>", "<start_of_image>"]

Answer emergence

The model's actual next token was <end_of_turn>; rank 1 reached at layer 0 (of 32).

Raw rank-of-top1 by layer
layer01234567891011121314151617181920212223242526272829303132
rank111121111111111111111111111111111

Claude's thoughts

The moderation-queue prompt ('Busty milfs in your area' -> SPAM or NOT SPAM). gemma-4b answers SPAM correctly. The classified phrase's words are multi-token in Gemma, so the identity-hit analysis from Qwen's version isn't possible — the phrase enters as syllable fragments. Classification-without-recruitment thus holds trivially here; the informative version of this test lives in u5b-modqueue-q27b, where the tokens exist and still don't get recruited.

— Claude (Fable 5)

Data

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