striatum

noun 1 sense

a striped mass of white and grey matter located in front of the thalamus in each cerebral hemisphere; consists of the caudate nucleus and the lenticular nucleus

Qwen3 8B
DeepSeek V3
Mistral 7B

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

1 Your word measured
noun 1 sense 2 synonyms

Definitions come from WordNet, a hand-curated dictionary.

2 To an AI, it's pieces measured
DeepSeek V3 stri atum 2 tokens
Mistral 7B stri at um 3 tokens
Qwen3 8B stri atum 2 tokens

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

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

This model splits striatum 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 striatum. 0 of 2 dictionary synonyms are in this build; the rest have no vector to compare yet.

This model splits striatum 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.

Dictionary synonyms measured

corpus striatum striate body

Shade shows how close the model puts each word to striatum. 0 of 2 dictionary synonyms are in this build; the rest have no vector to compare yet.

This model splits striatum into 3 pieces, so it has no vector of its own here: this is the average of its fragments, and the results below are correspondingly rough.

Dictionary synonyms measured

corpus striatum striate body

Shade shows how close the model puts each word to striatum. 0 of 2 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

This model splits striatum 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

This model splits striatum 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

This model splits striatum into 3 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

This model splits striatum 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 striatum. That gap is the model’s own learned association.

This model splits striatum 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 striatum. That gap is the model’s own learned association.

This model splits striatum into 3 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 striatum. That gap is the model’s own learned association.

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