{"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\/fr\/how-do-llms-understand-words-with-multiple-meanings\/","title":{"rendered":"Comment les MFR comprennent-ils les mots \u00e0 sens multiples ?"},"content":{"rendered":"
\"\"<\/figure>\n\n\n\n

Embo\u00eetements !<\/strong> Ils permettent de convertir des mots, des phrases ou m\u00eame des documents complets en repr\u00e9sentations num\u00e9riques (vecteurs) compr\u00e9hensibles par les ordinateurs. On peut consid\u00e9rer que les embeddings traduisent le langage humain sous une forme que les machines peuvent facilement traiter et analyser.<\/p>\n\n\n\n

Pour mieux comprendre les \"embeddings\", commen\u00e7ons par une simple analogie. Imaginons que vous rangiez des livres dans une biblioth\u00e8que. Vous pouvez placer les livres qui traitent d'un sujet similaire les uns \u00e0 c\u00f4t\u00e9 des autres. Par exemple, vous pouvez placer des livres scientifiques sur une \u00e9tag\u00e8re et des livres de cuisine sur une autre. Les embeddings fonctionnent de la m\u00eame mani\u00e8re en pla\u00e7ant des mots ou des concepts similaires ayant une signification similaire \u00e0 proximit\u00e9 les uns des autres dans un espace multidimensionnel.<\/p>\n\n\n\n

\n\n\n\n

Comment fonctionnent les Embeddings (simplement expliqu\u00e9s) :<\/h2>\n\n\n\n
    \n
  1. Transformez les mots en chiffres :\n
      \n
    • Chaque mot se voit attribuer un ensemble unique de nombres (un vecteur). Les mots ayant des significations similaires ont des vecteurs similaires.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
    • Mesurer la similitude :\n
        \n
      • En calculant la distance entre les vecteurs, les mod\u00e8les d'intelligence artificielle peuvent comprendre si des concepts ou des mots sont li\u00e9s. Des vecteurs plus proches signifient une plus grande similitude.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n

        Exemples pratiques d'embo\u00eetements :<\/h2>\n\n\n\n
          \n
        1. Recherche s\u00e9mantique :\n
            \n
          • Lorsque vous recherchez un terme qui a plusieurs significations, par exemple \"pomme\", le syst\u00e8me va faire la distinction entre l'entreprise et le fruit en fonction du contexte.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
          • Syst\u00e8mes de recommandation :\n
              \n
            • Des plateformes telles qu'Amazon ou Netflix utilisent les \"embeddings\" pour sugg\u00e9rer des articles similaires \u00e0 des films ou \u00e0 des produits que vous avez d\u00e9j\u00e0 appr\u00e9ci\u00e9s.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
            • Segmentation de la client\u00e8le :\n
                \n
              • Regroupement de clients en fonction de leur comportement ou de leurs centres d'int\u00e9r\u00eat afin d'am\u00e9liorer l'exp\u00e9rience client et de permettre un marketing personnalis\u00e9.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n

                La valeur commerciale des embo\u00eetements :<\/h2>\n\n\n\n
                  \n
                • Am\u00e9liorer l'efficacit\u00e9<\/strong> pour retrouver rapidement les informations pertinentes.<\/li>\n\n\n\n
                • Personnaliser les recommandations<\/strong>et d'am\u00e9liorer la satisfaction et l'engagement des clients.<\/li>\n\n\n\n
                • Obtenir des informations plus approfondies<\/strong> sur le comportement des clients gr\u00e2ce \u00e0 un regroupement efficace des donn\u00e9es.<\/li>\n<\/ul>\n\n\n\n
                  \n\n\n\n

                  Comprendre les embeddings permet d'exploiter plus efficacement l'IA g\u00e9n\u00e9rative. Comme nous l'avons vu, les embeddings sont tr\u00e8s puissants et aident les entreprises \u00e0 prendre des d\u00e9cisions plus \u00e9clair\u00e9es sur la base de donn\u00e9es. J'esp\u00e8re que cet article vous a aid\u00e9 \u00e0 mieux comprendre les embeddings dans l'IA g\u00e9n\u00e9rative !<\/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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