{"id":90,"date":"2025-03-10T12:55:37","date_gmt":"2025-03-10T16:55:37","guid":{"rendered":"https:\/\/businessphysics.ai\/?p=90"},"modified":"2025-03-17T13:53:01","modified_gmt":"2025-03-17T17:53:01","slug":"how-do-llms-understand-words-with-multiple-meanings","status":"publish","type":"post","link":"https:\/\/businessphysics.ai\/pt\/how-do-llms-understand-words-with-multiple-meanings\/","title":{"rendered":"Como os LLMs entendem palavras com v\u00e1rios significados?"},"content":{"rendered":"
\"\"<\/figure>\n\n\n\n

Incorpora\u00e7\u00f5es!<\/strong> S\u00e3o maneiras de converter palavras, frases ou at\u00e9 mesmo documentos completos em representa\u00e7\u00f5es num\u00e9ricas (vetores) que os computadores podem entender. Voc\u00ea pode pensar em embeddings como a tradu\u00e7\u00e3o da linguagem humana em uma forma que as m\u00e1quinas possam processar e analisar facilmente.<\/p>\n\n\n\n

Para entender melhor os embeddings, vamos come\u00e7ar com uma analogia simples. Digamos que voc\u00ea esteja organizando livros em uma biblioteca. Voc\u00ea pode colocar livros que tenham um t\u00f3pico semelhante um ao lado do outro. Por exemplo, voc\u00ea pode colocar livros de ci\u00eancias em uma prateleira e livros de culin\u00e1ria em outra. Os embeddings funcionam de maneira semelhante, colocando palavras ou conceitos semelhantes com significado semelhante pr\u00f3ximos uns dos outros em um espa\u00e7o multidimensional.<\/p>\n\n\n\n

\n\n\n\n

Como funcionam os Embeddings (explicado de forma simples):<\/h2>\n\n\n\n
    \n
  1. Transforme palavras em n\u00fameros:\n
      \n
    • Cada palavra recebe um conjunto exclusivo de n\u00fameros (um vetor). Palavras com significados semelhantes t\u00eam vetores semelhantes.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
    • Medindo a similaridade:\n
        \n
      • Ao calcular a dist\u00e2ncia entre os vetores, os modelos de IA podem entender se os conceitos ou as palavras est\u00e3o relacionados. Vetores mais pr\u00f3ximos significam mais similaridade.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n

        Exemplos pr\u00e1ticos de embeddings:<\/h2>\n\n\n\n
          \n
        1. Pesquisa sem\u00e2ntica:\n
            \n
          • Quando voc\u00ea pesquisa um termo que tem muitos significados, como, por exemplo, \"ma\u00e7\u00e3\", ele far\u00e1 a distin\u00e7\u00e3o entre a empresa e a fruta com base no contexto.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
          • Sistemas de recomenda\u00e7\u00e3o:\n
              \n
            • Plataformas como a Amazon ou a Netflix usam embeddings para sugerir itens semelhantes a filmes ou produtos de que voc\u00ea gostou anteriormente.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
            • Segmenta\u00e7\u00e3o de clientes:\n
                \n
              • Agrupamento de clientes com base em comportamento ou interesses para melhorar as experi\u00eancias do cliente e permitir o marketing personalizado.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n

                Valor comercial dos Embeddings:<\/h2>\n\n\n\n
                  \n
                • Melhorar a efici\u00eancia<\/strong> na recupera\u00e7\u00e3o r\u00e1pida de informa\u00e7\u00f5es relevantes.<\/li>\n\n\n\n
                • Personalizar recomenda\u00e7\u00f5es<\/strong>e aumentar a satisfa\u00e7\u00e3o e o envolvimento do cliente.<\/li>\n\n\n\n
                • Obtenha insights mais profundos<\/strong> no comportamento do cliente por meio de agrupamento eficaz de dados.<\/li>\n<\/ul>\n\n\n\n
                  \n\n\n\n

                  Entender os embeddings ajuda a aproveitar a IA generativa de forma mais eficaz. Como j\u00e1 discutimos, os embeddings s\u00e3o muito poderosos e ajudam as empresas a tomar decis\u00f5es mais informadas com base nos dados. Espero que este artigo tenha sido \u00fatil para melhorar sua compreens\u00e3o dos embeddings na IA generativa!<\/p>","protected":false},"excerpt":{"rendered":"

                  Embeddings! They are ways to convert words, sentences or even full fledged documents into numerical representations (vectors) that computers can understand. You can think of embeddings as translating human language into a form that machines can easily process and analyze. To better understand embeddings, let\u2019s start with a simple analogy. Let\u2019s say you are arranging […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[21,22,18,20,23,19,11,24],"class_list":["post-90","post","type-post","status-publish","format-standard","hentry","category-ai-article","tag-bert","tag-contextual-word-representations","tag-embeddings","tag-gpt","tag-semantic-understanding","tag-transformers","tag-understanding-ai","tag-word-embeddings"],"yoast_head":"\nHow Do LLMs Understand Words with Multiple Meanings? - Business Physics AI Lab<\/title>\n<meta name=\"description\" content=\"What trick do LLMs use to differentiate between two words that have a similar meaning ? Embeddings. How do they work ? 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