1
Your word
measured
“make a mess in one's office”
Definitions come from WordNet, a hand-curated dictionary.
make or cause to be or to become
作る is Japanese for create, 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.
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“make a mess in one's office”
Definitions come from WordNet, a hand-curated dictionary.
This panel splits 作る itself, not create — 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.
Shade shows how close the model puts each word to create, 1 of 8 also appear in the dictionary.
Shade shows how close the model puts each word to create, 1 of 8 also appear in the dictionary.
Shade shows how close the model puts each word to create, 1 of 8 also appear in the dictionary.
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.
Scores are projections onto axes we defined from anchor words, not labels the model assigns.
These sit just outside the closest neighbours in panel 3, and no dictionary lists any of them as related to create. 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 create. 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 create. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.