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Your word
measured
“special arrangements were available for the aged”
Definitions come from WordNet, a hand-curated dictionary.
people who are old collectively
Panels 3, 4 and 5 are computed per model. Switch to see them disagree.
“special arrangements were available for the aged”
Definitions come from WordNet, a hand-curated dictionary.
This is the tokenizer splitting text, not the model understanding it.
young is the dictionary opposite of elderly, yet this model puts it closer than 2 of the 3 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 elderly.
Shade shows how close the model puts each word to elderly, 3 of 8 also appear in the dictionary.
young is the dictionary opposite of elderly, yet this model puts it closer than every one of the 3 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 elderly, 2 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 elderly. 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 elderly. 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 elderly. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.