attention

noun 17 senses

(baseball) a failure by a batter or runner to reach a base safely in baseball

attention is French for out, and every measurement below was made on that English word. Definitions come from English WordNet; Open Multilingual WordNet supplies the French headword, not a French definition.

Qwen3 8B
DeepSeek V3
Mistral 7B

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

1 Your word measured

“you only get 3 outs per inning”

noun 17 senses 14 synonyms 1 antonyms

Definitions come from WordNet, a hand-curated dictionary.

2 To an AI, it's pieces measured
Qwen3 8B attention 1 token
DeepSeek V3 attention 1 token
Mistral 7B attention 1 token

This panel splits attention itself, not out — a tokenizer does not care what language it is fed. It is the only panel here that does.

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

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

Dictionary synonyms measured

away0.16 stunned0.03 forbidden0.02 prohibited0.01 taboo-0.00 come out of the closet come out extinct proscribed tabu verboten knocked out kayoed KO'd

Dictionary antonyms measured

safe is the dictionary opposite of out, yet this model puts it closer than 4 of the 5 synonyms it can score. Opposites share the sentences a word lives in, so cosine alone cannot tell them apart - which is why the dictionary seeds this list rather than the geometry.

Shade shows how close the model puts each word to out. 5 of 14 dictionary synonyms are in this build; the rest have no vector to compare yet.

Dictionary synonyms measured

away0.23 stunned0.01 forbidden-0.00 taboo-0.01 prohibited-0.02 come out of the closet come out extinct proscribed tabu verboten knocked out kayoed KO'd

Dictionary antonyms measured

safe is the dictionary opposite of out, yet this model puts it closer than 4 of the 5 synonyms it can score. Opposites share the sentences a word lives in, so cosine alone cannot tell them apart - which is why the dictionary seeds this list rather than the geometry.

Shade shows how close the model puts each word to out. 5 of 14 dictionary synonyms are in this build; the rest have no vector to compare yet.

Dictionary synonyms measured

away0.22 taboo0.07 forbidden0.05 stunned0.05 prohibited0.04 come out of the closet come out extinct proscribed tabu verboten knocked out kayoed KO'd

Dictionary antonyms measured

safe is the dictionary opposite of out, yet this model puts it closer than 3 of the 5 synonyms it can score. Opposites share the sentences a word lives in, so cosine alone cannot tell them apart - which is why the dictionary seeds this list rather than the geometry.

Shade shows how close the model puts each word to out. 5 of 14 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
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 out. 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 out. 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 out. That gap is the model’s own learned association.

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