1
Your word
measured
“The amount of work increased”
Definitions come from WordNet, a hand-curated dictionary.
become bigger or greater in amount
augmenter is French for increased, 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.
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“The amount of work increased”
Definitions come from WordNet, a hand-curated dictionary.
This panel splits augmenter itself, not increased — 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.
decreased is the dictionary opposite of increased, yet this model puts it closer than every one of the 1 synonym 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 increased, 1 of 8 also appear in the dictionary.
Shade shows how close the model puts each word to increased, 1 of 8 also appear in the dictionary.
Shade shows how close the model puts each word to increased, 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 increased. 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 increased. 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 increased. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.