How Word Vectors Can Encode Relationships

A brief introduction to embeddings and the geometric analogies they can exhibit.

Canal
3Blue1Brown
Publicado
11 abr 2024
Vídeo
1:00
Lectura
1 min
Original
inglés
Abrir en YouTube
Al desplazarte, el vídeo se reduce a la esquina inferior derecha y sigue reproduciéndose. Los subtítulos siguen tu idioma de lectura.
Idioma de lecturaDisponible en 2 idiomas
Este artículo aún no está en español, así que se muestra el original en inglés.
RESUMEN

En 30 segundos

3Blue1Brown explains how text tokens are associated with vectors and illustrates relationships through vector arithmetic, while such analogies depend on the particular embedding.

  1. 1EmbeddingA token is associated with a vector in a high-dimensional space.
  2. 2Vector relationshipsSome word relationships appear as differences between vectors.
  3. 3An illustration, not a ruleThe arithmetic depends on the embedding and does not always work.

Conclusiones

  1. Tokens can be represented by high-dimensional vectors.
  2. Learned vector geometry can reflect relationships between words.
  3. Analogy arithmetic is illustrative and does not universally hold.
TEXTO COMPLETO

Lee el texto editado por capítulos

Solo la transcripción
01

Tokens and vectors

A short 3Blue1Brown explanation asks how word vectors can reflect relationships between concepts. It is an excerpt from a longer video about language models.

A short 3Blue1Brown explanation asks how word vectors can reflect relationships between concepts. It is an excerpt from a longer video about language models.

A language model divides text into pieces called tokens and associates them with vectors: long lists of numbers. An embedding can be imagined as a direction or position in a high-dimensional space.

02

Relationships in space

Some learned embeddings encode relationships in their geometry. The example in the video says that a difference between gender-related word vectors can move the vector for “uncle” near that for “aunt.”

Some learned embeddings encode relationships in their geometry. The example in the video says that a difference between gender-related word vectors can move the vector for “uncle” near that for “aunt.”

03

Limits of analogy

The narrator gives another historical analogy involving Italy, Germany, Hitler and Mussolini. Such arithmetic illustrates a pattern in a particular embedding space; it is not a reliable rule for every model or pair of words.

The narrator gives another historical analogy involving Italy, Germany, Hitler and Mussolini. Such arithmetic illustrates a pattern in a particular embedding space; it is not a reliable rule for every model or pair of words.

Más de este canal

20 diapositivas40:44EN
3Blue1Brown· 16 oct 2026Grant Sanderson: There's never been a better time to go into mathGrant Sanderson, Constantin Kogler · 3Blue1BrownStudents fear AI has solved mathematics. Grant Sanderson argues the opposite: even if machines prove theorems better than we do, the institution of math stays valuable—and this is a rare moment when newcomers can reshape it.MathematicsAI for scienceHuman-AI Collaboration+2
29 diapositivas51:55EN
3Blue1Brown· 18 sept 2026The last IMO problem AI could not solveGrant Sanderson · 3Blue1BrownGrant Sanderson walks through IMO 2025 Problem 6 — a tiling puzzle that frontier models missed — and the visual patience it takes to prove a construction is optimal.MathematicsAI researchReasoning & test-time compute+2
0:55EN
3Blue1Brown· 24 jul 2026The 64 Sugar Cubes Puzzle: Six Equal DistancesA 4×4×4 block has three colors; prove that six same-colored pairs share a distance.MathematicsEducation
33:51EN
3Blue1Brown· 16 jul 2026What Cross-Entropy Connects: Compression, Language Models and KL DivergenceGrant Sanderson · 3Blue1BrownGrant Sanderson builds cross-entropy from code lengths and uses it to explain next-token training and distillation.MathematicsMachine LearningFoundation Models+1
Diapositivas2:25EN
3Blue1Brown· 12 jun 2026How Shannon Measured the Entropy of EnglishA guessing game helped Claude Shannon estimate how much information English text contains, and why prediction is central to compression.MathematicsAI History
Diapositivas1:02EN
3Blue1Brown· 10 jun 2026A Robot Walk and the Puzzle of Optimal Binary EncodingHow few bits does it take to send directions that occur with unequal frequencies?MathematicsScience Communication