Cet article est le premier d’une série qui vont couvrir les utilisations possibles de ChatGPT dans la vie de tous les jours. Aujourd’hui, nous commençons par les applications analytiques sur un texte, qui sont ici regroupées en trois cas concrets : demande de résumé, extraction d’informations, exploitation des connaissances propres du modèle. La possibilité assez fascinante de dialoguer avec un document est également évoquée.
Dans les prochains articles, je couvrirai successivement les applications transformatives (traduction, réécriture), puis les applications génératives (création). Ensuite, je couvrirai la création automatisée de programmes informatiques, pour terminer sur quelques réflexions sur l’exploitation pratique de ces modèles.
1. Demander le résumé d’un texte
Résumer un texte est une tâche tout à fait à la mesure des modèles génératifs, qui se débrouillent en général assez bien dans ce genre d’exercise. Afin d’obtenir les résultats désirés, il est vivement conseillé de bien circonscrire le texte à résumer au moyen de délimiteurs (« … » ou autres), puis de bien qualifier le type de résumé que vous désirez.
Cette qualification peut se faire de nombreuses manières, dont voici quelques exemples :
- limiter la longueur de la réponse, par exemple : ‘résumer en 200 mots maximum’
- orienter le résumé, par exemple: ‘résumer le projet de manière positive et favorable à son extension ‘
- spécifier un style particulier, par exemple : ‘résumer dans le style d’un enfant de huit ans’
- changer la langue : « résumer en Anglais »
Attention cependant que ceci ne fonctionne pour des textes de taille inférieure à celle de la fenêtre de contexte, réponse comprise. En pratique, nous sommes limités à des textes d’ environ une dizaine de pages et un long article ou un livre sont encore hors d’ atteinte. Je vous renvoie au texte précédent qui traite plus en détail des fenêtres de contexte.
2. Analyse d’un texte
Il s’agit ici de demander au modèle une ou plusieurs questions spécifiques relatives à un texte fourni. Ceci va faire appel aux capacités de raisonnement et d’analyse du modèle. Attention que les résultats ne sont pas toujours fiables. Si le système semble confus, n’hésitez pas à lui demander d’expliquer son raisonnement étape par étape et/ou à lui montrer des exemples de problèmes similaires résolus.
Une chose assez merveilleuse est que vous pouvez dialoguer interactivement avec le système au sujet du texte et poser de multiples questions. Cette capacité laisse entrevoir d’énormes potentialités, notamment en termes d’éducation (imaginez que vous pouvez poser interactivement des question à un syllabus de cours !).
Il existe aujourd’hui des sites web comme celui-ci sur lequel vous pouvez télécharger un fichier pdf puis dialoguer avec. Si vous possédez l’abonnement ChatGPT Plus, le plugin AskYourPDF permet d’en faire autant.
3. Extraction de la connaissance propre du modèle
Les Grands Modèles de Langage ont été entraînés sur un volume de données gigantesque qui dépasse de très loin ce qu’un être humain peut lire au cours d’une vie entière. Le modèle possède donc une base de connaissances extrêmement large qui en fait une sorte de Pic de la Mirandole sans qu’il soit nécessaire de lui fournir un quelconque texte en entrée, et qui est ouverte à vos questions et dialogues.
Attention cependant que la véracité de la réponse n’est pas garantie, il vous revient donc vérifier l’exactitude de la réponse avant toute exploitation du résultat !
Une autre limitation de ces modèles est liée au fait que les bases de données d’entraînement ont été collectées à un moment précis dans le temps, et le système ignore tout ce qui s’est déroulé ensuite. Sa connaissance des événements récents est donc sujette à caution. Les nouveaux modèles comme ChatGPT4 et Bing Chat permettent de pallier à cet inconvénient en lançant automatiquement des recherches sur le Web pour s’assurer que leur réponse est à jour (ils indiquent d’ailleurs dans le dialogue les recherches qu’ ils lancent).
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