{"id":10846,"date":"2024-11-27T15:32:02","date_gmt":"2024-11-27T15:32:02","guid":{"rendered":"https:\/\/novelis.io\/?post_type=research-lab&p=10846"},"modified":"2024-11-27T15:35:03","modified_gmt":"2024-11-27T15:35:03","slug":"reduire-les-hallucinations-de-lia-grace-a-des-donnees-fiables-du-monde-reel","status":"publish","type":"research-lab","link":"https:\/\/novelis.io\/fr\/research-lab\/reduire-les-hallucinations-de-lia-grace-a-des-donnees-fiables-du-monde-reel\/","title":{"rendered":"R\u00e9duire les hallucinations de l’IA gr\u00e2ce \u00e0 des donn\u00e9es fiables du monde r\u00e9el"},"content":{"rendered":"\n

Malgr\u00e9 les capacit\u00e9s impressionnantes des grands mod\u00e8les de langage (LLM), ces derniers peuvent parfois g\u00e9n\u00e9rer avec assurance des informations inexactes, <\/strong>un ph\u00e9nom\u00e8ne connu sous le nom d\u2019\u00ab hallucination \u00bb. <\/strong>Ce probl\u00e8me repr\u00e9sente un d\u00e9fi majeur pour l\u2019IA G\u00e9n\u00e9rative<\/a>, en particulier lorsqu\u2019il s\u2019agit de donn\u00e9es num\u00e9riques et statistiques. Ces donn\u00e9es posent des d\u00e9fis sp\u00e9cifiques :<\/p>\n\n\n\n

    \n
  1. Complexit\u00e9 des op\u00e9rations statistiques<\/strong> : L\u2019entra\u00eenement des mod\u00e8les sur des requ\u00eates li\u00e9es \u00e0 des informations statistiques implique souvent des op\u00e9rations logiques, arithm\u00e9tiques ou de comparaison, avec des niveaux de complexit\u00e9 vari\u00e9s.<\/li>\n\n\n\n
  2. Formats vari\u00e9s et contexte n\u00e9cessaire<\/strong> : Les donn\u00e9es statistiques publiques existent dans des formats et des sch\u00e9mas divers, n\u00e9cessitant fr\u00e9quemment une interpr\u00e9tation bas\u00e9e sur un contexte pr\u00e9cis. Cela rend leur utilisation particuli\u00e8rement difficile pour les syst\u00e8mes utilisant la g\u00e9n\u00e9ration augment\u00e9e par r\u00e9cup\u00e9ration (RAG).<\/li>\n<\/ol>\n\n\n\n
    <\/div>\n\n\n\n

    DataGemma : une solution innovante<\/h4>\n\n\n\n

    Des chercheurs de Google ont d\u00e9velopp\u00e9 DataGemma<\/strong>, un outil qui connecte les LLM \u00e0 Data Commons \u2014 un vaste r\u00e9f\u00e9rentiel unifi\u00e9 de donn\u00e9es statistiques publiques \u2014 afin de relever ces d\u00e9fis. Deux approches distinctes sont utilis\u00e9es : RIG (Retrieval-Interleaved Generation)<\/strong> et RAG (Retrieval-Augmented Generation)<\/strong>. Ces m\u00e9thodes s\u2019appuient sur les mod\u00e8les open source de Google, Gemma<\/strong> et Gemma-2<\/strong>, qui sont ajust\u00e9s sp\u00e9cifiquement pour ces approches.<\/p>\n\n\n\n

    <\/div>\n\n\n\n

    Points-cl\u00e9s de DataGemma<\/h4>\n\n\n\n

    1. Data Commons<\/strong> : Ce r\u00e9f\u00e9rentiel figure parmi les plus grands au monde pour les donn\u00e9es statistiques publiques, avec plus de 240 milliards de points de donn\u00e9es couvrant des centaines de milliers de variables statistiques. Les sources de donn\u00e9es incluent des organisations reconnues comme l\u2019OMS, l\u2019ONU, le CDC (Centers for Disease Control and Prevention) et les bureaux de recensement.<\/p>\n\n\n\n

    2. RIG (Retrieval-Interleaved Generation)<\/strong> : Cette approche am\u00e9liore les capacit\u00e9s de Gemma-2 en interrogeant activement des sources fiables et en utilisant les donn\u00e9es de Data Commons pour v\u00e9rifier les faits. Lorsqu\u2019on demande \u00e0 DataGemma de produire une r\u00e9ponse, le mod\u00e8le identifie d\u2019abord les \u00e9l\u00e9ments n\u00e9cessitant des donn\u00e9es statistiques, puis r\u00e9cup\u00e8re ces informations dans Data Commons. Bien que la m\u00e9thodologie RIG soit d\u00e9j\u00e0 connue, son int\u00e9gration dans le cadre de DataGemma est une innovation.<\/p>\n\n\n\n

    3. RAG (Retrieval-Augmented Generation)<\/strong> : Cette m\u00e9thode permet aux mod\u00e8les linguistiques d\u2019acc\u00e9der \u00e0 des informations externes pertinentes en compl\u00e9ment des donn\u00e9es d\u2019entra\u00eenement, leur fournissant un contexte plus riche et leur permettant de g\u00e9n\u00e9rer des r\u00e9ponses plus d\u00e9taill\u00e9es et pr\u00e9cises. DataGemma met en \u0153uvre cette m\u00e9thode en exploitant la fen\u00eatre de contexte \u00e9tendue du mod\u00e8le Gemini 1.5 Pro. Avant de g\u00e9n\u00e9rer une r\u00e9ponse, DataGemma r\u00e9cup\u00e8re des informations pertinentes depuis Data Commons, ce qui r\u00e9duit les risques d\u2019hallucination et am\u00e9liore l\u2019exactitude des r\u00e9ponses.<\/p>\n\n\n\n

    <\/div>\n\n\n\n

    R\u00e9sultats prometteurs<\/h4>\n\n\n\n

    Bien que ces approches en soient encore \u00e0 leurs d\u00e9buts, les r\u00e9sultats initiaux sont encourageants. Les chercheurs rapportent des am\u00e9liorations significatives dans la gestion des donn\u00e9es num\u00e9riques<\/strong> par les mod\u00e8les linguistiques. Les utilisateurs devraient donc constater une r\u00e9duction des hallucinations, ce qui rend ces mod\u00e8les plus fiables pour la recherche, la prise de d\u00e9cision et les questions g\u00e9n\u00e9rales.<\/p>\n\n\n\n

    <\/div>\n\n\n
    \n
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