1
Your word
measured
“the relationship between mothers and their children”
Definitions come from WordNet, a hand-curated dictionary.
a relation between people; (`relationship' is often used where `relation' would serve, as in `the relationship between inflation and unemployment', but the preferred usage of `relationship' is for human relations or states of relatedness)
縁 is Japanese for relationship, 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.
“the relationship between mothers and their children”
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.
2 of those are a raw byte rather than a character: DeepSeek V3 splits this word below the character, so no single token it sees is readable.
3 of those are a raw byte rather than a character: Mistral 7B splits this word below the character, so no single token it sees is readable.
This panel splits 縁 itself, not relationship — 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 relationship. 0 of 3 dictionary synonyms are in this build; the rest have no vector to compare yet.
Shade shows how close the model puts each word to relationship. 0 of 3 dictionary synonyms are in this build; the rest have no vector to compare yet.
Shade shows how close the model puts each word to relationship. 0 of 3 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.
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 relationship. 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 relationship. 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 relationship. That gap is the model’s own learned association.
These are statistical associations in the training data, not the model thinking.