従う

verb 8 senses

choose and follow; as of theories, ideas, policies, strategies or plans

従う is Japanese for adopted, and every measurement below was made on that English word. Definitions come from English WordNet; Open Multilingual WordNet supplies the Japanese headword, not a Japanese 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

“She followed the feminist movement”

verb 8 senses 16 synonyms 1 antonyms

Definitions come from WordNet, a hand-curated dictionary.

2 To an AI, it's pieces measured
Qwen3 8B byte byte 3 tokens

2 of those are a raw byte rather than a character: Qwen3 8B splits this word below the character, so no single token it sees is readable.

DeepSeek V3 2 tokens
Mistral 7B byte byte byte 4 tokens

3 of those are a raw byte rather than a character: Mistral 7B splits this word below the character, so no single token it sees is readable.

This panel splits 従う itself, not adopted — 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 antonyms measured

native is the dictionary opposite of adopted, yet this model puts it closer than 2 of the 7 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 adopted, 1 of 8 also appear in the dictionary. 7 of 16 dictionary synonyms are in this build; the rest have no vector to compare yet.

Dictionary antonyms measured

Shade shows how close the model puts each word to adopted, 1 of 8 also appear in the dictionary. 7 of 16 dictionary synonyms are in this build; the rest have no vector to compare yet.

Dictionary antonyms measured

native is the dictionary opposite of adopted, yet this model puts it closer than 2 of the 7 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 adopted, 1 of 8 also appear in the dictionary. 7 of 16 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 adopted. 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 adopted. 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 adopted. That gap is the model’s own learned association.

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