smash

noun 16 senses

a vigorous blow

Qwen3 8B
DeepSeek V3
Mistral 7B

Panels 3, 4 and 5 are computed per model. Switch to see them disagree.

1 Your word measured

“the sudden knock floored him”

noun 16 senses 22 synonyms

Definitions come from WordNet, a hand-curated dictionary.

2 To an AI, it's pieces measured
DeepSeek V3 smash 1 token
Mistral 7B sm ash 2 tokens
Qwen3 8B smash 1 token

This is the tokenizer splitting text, not the model understanding it.

3 Synonyms, antonyms, and the AI measured
Qwen3 8B
DeepSeek V3
Mistral 7B

Shade shows how close the model puts each word to smash. 16 of 22 dictionary synonyms are in this build; the rest have no vector to compare yet.

Shade shows how close the model puts each word to smash, 3 of 8 also appear in the dictionary. 16 of 22 dictionary synonyms are in this build; the rest have no vector to compare yet.

This model splits smash into 2 pieces, so it has no vector of its own here: this is the average of its fragments, and the results below are correspondingly rough.

Shade shows how close the model puts each word to smash. 16 of 22 dictionary synonyms are in this build; the rest have no vector to compare yet.

Model neighbours are distributional, not dictionary synonyms. Two words can be close because they appear in similar sentences, which is why an antonym can outscore a synonym here.

4 Its personality measured
Qwen3 8B
DeepSeek V3
Mistral 7B
rare common
concrete abstract
casual formal
everyday technical
negative positive
mild intense
powerless powerful
small big
rare common
concrete abstract
casual formal
everyday technical
negative positive
mild intense
powerless powerful
small big

This model splits smash into 2 pieces, so it has no vector of its own here: this is the average of its fragments, and the results below are correspondingly rough.

rare common
concrete abstract
casual formal
everyday technical
negative positive
mild intense
powerless powerful
small big

Scores are projections onto axes we defined from anchor words, not labels the model assigns.

5 Surprising neighbours measured
Qwen3 8B
DeepSeek V3
Mistral 7B

These sit just outside the closest neighbours in panel 3, and no dictionary lists any of them as related to smash. That gap is the model’s own learned association.

These sit just outside the closest neighbours in panel 3, and no dictionary lists any of them as related to smash. That gap is the model’s own learned association.

This model splits smash into 2 pieces, so it has no vector of its own here: this is the average of its fragments, and the results below are correspondingly rough.

These sit just outside the closest neighbours in panel 3, and no dictionary lists any of them as related to smash. That gap is the model’s own learned association.

These are statistical associations in the training data, not the model thinking.