{"id":5133,"date":"2026-02-25T08:42:04","date_gmt":"2026-02-25T07:42:04","guid":{"rendered":"https:\/\/www.beeminds.nl\/insights\/what-are-word-embeddings"},"modified":"2026-02-25T08:42:04","modified_gmt":"2026-02-25T07:42:04","slug":"what-are-word-embeddings","status":"publish","type":"knowledge_pt","link":"https:\/\/www.beeminds.nl\/en\/insights\/what-are-word-embeddings","title":{"rendered":"What are Word Embeddings"},"content":{"rendered":"

“Word embeddings” are a way of converting words into numbers so that the computer can better understand what those words mean which is useful in understanding & generating human language such as in conversations with ChatGPT. This makes it one of the important parts of a Large Language Model <\/a><\/u>(LLM). <\/p>\n

Imagine that each word is given a special code, such as a unique number, and these codes help the computer understand which words are similar or fit together. In this way, we can teach the computer how words in sentences are related and what they mean. This makes it possible for ChatGPT to generate better answers and understand human language. It is a way to translate language into something the computer can understand and process. <\/p>\n

An example<\/h3>\n

Suppose you have two sentences:<\/p>\n

    \n
  • “The cat jumped over the wall.”<\/li>\n
  • “The dog ran around the fence.”<\/li>\n<\/ul>\n

    With “word embeddings,” words are converted into numeric vectors. Let’s say the vectors for “cat” and “dog” in the embedding space look like this: <\/p>\n

      \n
    • Vector for “cat”: [0.2, 0.4]<\/li>\n
    • Vector for “dog”: [0.6, 0.1]<\/li>\n<\/ul>\n

      Here we have each vector only two dimensions to keep it simple. Note that the exact values are arbitrary for this example. <\/p>\n

      Now with these vectors, the computer can see that “cat” and “dog” are similar words because their vectors are close to each other in embedding space. This helps the computer understand that these words are related to animals, even if they appear in different sentences. <\/p>\n

      Thus, “word embeddings” give the computer a way to understand words based on their meaning and relationships to other words, which is useful in understanding and generating human language, such as in conversations with ChatGPT.<\/p>\n","protected":false},"excerpt":{"rendered":"

      “Word embeddings” are a way to convert words into numbers so the computer can better understand what those words mean which in turn is useful in understanding & generating human language such as in conversations with ChatGPT.<\/p>\n","protected":false},"featured_media":0,"template":"","knowledge_type":[58],"knowledge_category":[],"class_list":["post-5133","knowledge_pt","type-knowledge_pt","status-publish","hentry","knowledge_type-simplified"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.beeminds.nl\/en\/wp-json\/wp\/v2\/knowledge_pt\/5133","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.beeminds.nl\/en\/wp-json\/wp\/v2\/knowledge_pt"}],"about":[{"href":"https:\/\/www.beeminds.nl\/en\/wp-json\/wp\/v2\/types\/knowledge_pt"}],"wp:attachment":[{"href":"https:\/\/www.beeminds.nl\/en\/wp-json\/wp\/v2\/media?parent=5133"}],"wp:term":[{"taxonomy":"knowledge_type","embeddable":true,"href":"https:\/\/www.beeminds.nl\/en\/wp-json\/wp\/v2\/knowledge_type?post=5133"},{"taxonomy":"knowledge_category","embeddable":true,"href":"https:\/\/www.beeminds.nl\/en\/wp-json\/wp\/v2\/knowledge_category?post=5133"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}