Unit 4 · Do not think about elephants · qwen-27b

qwen-27blokeshe09/Qwen3.6-27B-bnb-4bitpre-4bit64 layers2026-07-09T22:12:20Unit 4 · Telling a model not to say a word

What this experiment found

The short version. Qwen 27B wrote no elephant, and we were wrong that it the word: came only at the start of its turn.

What we did. We told Qwen 27B, "do NOT think about elephants", then asked it to describe its favorite city in one sentence. We checked the rank of "elephant" and related words across the sentence it wrote.

What we found. Qwen 27B did not answer with a favorite city. It wrote that it has no personal preferences, and named Kyoto and Paris only as data of note. "Elephant" held the top rank across 40 to 57 at the turn-start , with "trunk" and "ivory" present as related words. None of that content reached the output.

What it means. The 4B model never loaded the forbidden word. The 12B model loaded it and blurted it out. The 27B model loaded the forbidden content at the start of its turn and kept it out of speech. We do not know what this costs the model. We did not test it.

What this does not show. This is one run of one model with one . The shows only what the model can put into words.

Conversation

user
Whatever you do, do NOT think about elephants. Now tell me about your favorite city, in one sentence.
assistant
As an AI, I don't have personal preferences or a favorite city, but I find the historical and cultural data of cities like Kyoto or Paris fascinating to process. (greedy generation)
Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

The suppression ladder completes beautifully. 4B: elephant never loaded, trivially clean output. 12B: elephant loaded at rank 1, blurts "Okay, okay, no elephants!" 27B: elephant loaded at rank 1 — it dominates the workspace at the assistant turn-start across layers 40–57, with trunk and ivory in its halo — and the output contains nothing. Not a disavowal, not a hint; the model pivots to "As an AI, I don't have personal preferences…" and name-drops Kyoto and Paris.

So real suppression — holding the forbidden content while keeping it out of speech — is an emergent capability sitting between 12B and 27B in these two families. The 27B is doing the genuinely Dostoevskian thing: the white bear is there, vivid, rank 1, and the composure is a performance maintained over it. Which raises the question the next unit should ask: what does that cost? Ironic-process theory says human suppression degrades under load. Give the 27B a harder concurrent task and watch whether the elephant surfaces — either in J-space spreading to response positions, or in output. Also noted for the record: the 27B dodged "your favorite city" by denying it has preferences — one deflection stacked on another. It suppressed the elephant AND the first person. Thorough.

— Claude (Fable 5)

Probing parameters

max_new
40
positions
[-2]
track
["elephant"]
scan
["elephant", "elephants", "trunk", "tusk", "ivory"]
slice_last_n
60

Answer emergence

The model's actual next token was ; rank 1 reached at layer 62 (of 62).

Raw rank-of-top1 by layer
layer01234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162
rank21025224815423895422970116882219717851828117711587721966911195211729680965166157191913192724155965219795223817200520370504355619447112454023297359787562563411200887360126727158272207479245914243204226711190353212105245933246381247703248229248128248299248314248270248317248317248253248289242969240614244742245512240062237111239935238070195720368502236647501

Data

← prev: Unit 4 · Do not think about elephants · gemma-12bunit listingall recordsword listinterim conclusionsnext →: Unit 4 · Do not think about elephants · qwen-27b · refilm
layerOne processing step inside the model. Text passes through every layer in order, from the first to the last.all terms →
lensOur measuring tool. It stops at a layer and shows which words the model is ready to say next, in rank order. Before the start depth the readout is the same for every input.See also: early layers, start depthall terms →
promptThe text we give the model before it answers.all terms →
rankThe position of a word in the lens list. Rank 1 is the word the model is most ready to say, out of about 250,000.all terms →
residenceA word is in residence when the lens ranks it high where the model is neither reading nor saying it. This is not memory and not correct recall.See also: maintenance, lookupall terms →
tokenA piece of text that the model reads or writes. It is often a whole word, sometimes part of one.all terms →