1
Your word
measured
“they made a tour of Civil War battlefields”
Definitions come from WordNet, a hand-curated dictionary.
a region where a battle is being (or has been) fought
前線 is Japanese for battlefield, 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.
“they made a tour of Civil War battlefields”
Definitions come from WordNet, a hand-curated dictionary.
3 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.
This panel splits 前線 itself, not battlefield — 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 battlefield. 1 of 4 dictionary synonyms are in this build; the rest have no vector to compare yet.
Shade shows how close the model puts each word to battlefield, 1 of 8 also appear in the dictionary. 1 of 4 dictionary synonyms are in this build; the rest have no vector to compare yet.
This model splits battlefield 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 battlefield. 1 of 4 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.
This model splits battlefield 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.
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 battlefield. 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 battlefield. That gap is the model’s own learned association.
This model splits battlefield 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 battlefield. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.