{"id":1147,"date":"2023-11-24T11:50:03","date_gmt":"2023-11-24T11:50:03","guid":{"rendered":"https:\/\/imnet.com\/it\/?p=1147"},"modified":"2023-11-24T14:53:15","modified_gmt":"2023-11-24T14:53:15","slug":"come-aumentare-laffidabilita-dei-chatbot-basati-su-llm","status":"publish","type":"post","link":"https:\/\/imnet.com\/it\/come-aumentare-laffidabilita-dei-chatbot-basati-su-llm\/","title":{"rendered":"Come aumentare l\u2019affidabilit\u00e0 dei chatbot basati su LLM"},"content":{"rendered":"

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ChatGPT e i suoi fratelli vengono usati per sempre pi\u00f9 servizi per il pubblico. \u00c8 affascinante e terrificante nello stesso tempo. Come possiamo eliminare il terrore?<\/h2>\n

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Nel Corso dell\u2019ultimo anno, dopo il primo rilascio di ChatGPT, c\u2019\u00e8 stata un\u2019esplosione di chatbot basati su Large Language Models (LLM). L\u2019adozione della tecnologia LLM nell\u2019intelligenza artificiale conversazionale sta davvero rivoluzionando il settore, con un\u2019esperienza dell\u2019utente molto migliore di quella che era prima. Questi chatbot promettono meraviglie per applicazioni molto diverse, dal servizio al cliente alla traduzione simultanea alla sanit\u00e0, insomma per qualunque servizio per fornire informazioni al pubblico in linguaggio naturale, sia a voce che per scritto.<\/p>\n

Ma non tutto \u00e8 perfetto nel campo dell\u2019IA per il servizio ai clienti: rimangono difetti piuttosto gravi. In quest\u2019articolo cerco di spiegare alcuni dei problemi principali e suggerire possibili soluzioni per mitigarli, se non risolverli.<\/p>\n

La predicibilit\u00e0 dei \u201cLarge Language Models\u201d<\/strong><\/h2>\n

Gli LLM sono collezioni enormi di frammenti di informazione, che l\u2019algoritmo di intelligenza artificiale collega in maniera statistica. Per fornire le risposte, l\u2019algoritmo prende il cammino pi\u00f9 probabile per andare da un frammento ad un altro, a seconda della domanda e considerando il suo contesto. Questo processo non porta sempre a un risultato predicibile, come capita di solito quando si gioca sulla statistica.<\/p>\n

Per certi casi d\u2019uso, non \u00e8 un problema insormontabile se le risposte da un chatbot basato su LLM cambiano da una all\u2019altra. Per esempio, il riassunto di una conversazione pu\u00f2 essere reso in vari modi, senza mancare di accuratezza. Oppure un prodotto pu\u00f2 essere raccomandato da un algoritmo basato su LLM con frasi e parole diverse.<\/p>\n

Ma ci sono altre applicazioni, pi\u00f9 collegate all\u2019assistenza al cliente, dove la precisione delle risposte \u00e8 fondamentale e un errore pu\u00f2 avere un costo alto: ogni volta che ci possono essere conseguenze legali, per esempio, o quando il chatbot viene usato come il primo livello di interazione quando l\u2019utente cerca di avere un prestito. In questi casi, mentre la promessa degli algoritmi basati su LLM \u00e8 chiara, bisogna ridurre il rischio.<\/p>\n

Sensibilit\u00e0 dei chatbot LLM al loro input<\/strong><\/h2>\n

La lingua che usiamo \u00e8 fluida, e ci sono normalmente molti modi di dire la stessa cosa. Come nelle altre lingue, in italiano ci sono modi di dire, metafore e sinonimi che tutti usano, magari senza rendersene conto. Quindi persone diverse useranno parole diverse in diversi momenti per fare una domanda a un chatbot, anche secondo il livello di istruzione del parlante, il suo stato d\u2019animo, la sua et\u00e0, dove abita, eccetera.<\/p>\n

Naturalmente questo complica le cose assai quando si parla con un chatbot basato su LLM, che dipende dalla frase in ingresso per trovare la risposta: la stessa domanda pu\u00f2 assumere significati completamente diversi a seconda delle parole usate. Il risultato \u00e8 che, almeno per ora, quando la precisione \u00e8 importante non ci si pu\u00f2 fidare un granch\u00e9 di un chatbot basato su LLM per il servizio clienti.<\/p>\n

Una soluzione: standardizzare l\u2019input<\/strong><\/h2>\n

Mandare a un chatbot basato su LLM sempre le stesse parole per la stessa domanda sarebbe un bel passo avanti nel risolvere il problema della precisione delle risposte. Questo non \u00e8 ovviamente possibile quando il chatbot risponde a domande qualsiasi, con un argomento libero. Ma \u00e8 possibile, ed anche relativamente facile, se le domande e le risposte si riferiscono a un dominio preciso, come quello del servizio ai clienti. In questo caso, proponiamo di piazzare un sistema di call steering, che usa il linguaggio naturale per determinare l\u2019intento degli utenti, davanti al chatbot basato su LLM. Quando il sistema di call steering determina l\u2019intento del chiamante, anche attraverso un dialogo abbastanza complesso, manda al chatbot una domanda standardizzata per quell\u2019intento, che sar\u00e0 stata ottimizzata per produrre la risposta pi\u00f9 utile.<\/p>\n

Interactive Media ha molta esperienza di applicazioni conversazionali, e ha una struttura e un processo standard per l\u2019implementazione di applicazioni di call steering. Quindi, abbiamo messo a punto una piattaforma completa per aiutare gli utenti ad interagire con un chatbot LLM in applicazioni di servizio al cliente.\u00a0Abbiamo integrato PhoneMyBot con la nostra piattaforma di call steering MIND e con il chatbot LLM, che svolge la parte di servizio al cliente vera e propria. Quando un utente chiama il numero del servizio, risponde \u00a0MIND, che fa delle domande per individuare con precisione l\u2019intento della chiamata. Una volta fatto questo, MIND manda a PhoneMyBot il testo della domanda da fare al chatbot. PhoneMyBot poi riceve la risposta dal chatbot e la passa all\u2019utente.<\/p>\n

Questa tecnica aumenta la qualit\u00e0 e la precisione delle risposte dei chatbot LLM, rendendoli piu\u2019 adatti a lavorare in un ambiente di servizio al cliente.<\/p>\n

Ci farebbe molto piacere parlarvi di questa soluzione e darvi maggiori dettagli: se interessati, contattate Interactive Media a info@imnet.com<\/a> o cliccate sul pulsante qui sotto.<\/p>\n

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ChatGPT e i suoi fratelli vengono usati per sempre pi\u00f9 servizi per il pubblico. \u00c8 affascinante e terrificante nello stesso tempo. Come possiamo eliminare il terrore?Nel Corso dell\u2019ultimo anno, dopo il primo rilascio di ChatGPT, c\u2019\u00e8 stata un\u2019esplosione di chatbot basati su Large Language Models (LLM). L\u2019adozione della tecnologia LLM nell\u2019intelligenza artificiale conversazionale sta davvero rivoluzionando il settore, con un\u2019esperienza dell\u2019utente molto migliore di quella che era prima. Questi chatbot promettono meraviglie per applicazioni molto diverse, dal servizio al cliente alla traduzione simultanea alla sanit\u00e0, insomma per qualunque servizio per fornire informazioni al pubblico in linguaggio naturale, sia a voce che per scritto. Ma non tutto \u00e8 perfetto nel campo dell\u2019IA per il servizio ai clienti: rimangono difetti piuttosto gravi. In quest\u2019articolo cerco di spiegare alcuni dei problemi principali e suggerire possibili soluzioni per mitigarli, se non risolverli. La predicibilit\u00e0 dei \u201cLarge Language Models\u201d Gli LLM sono collezioni enormi di frammenti di informazione, che l\u2019algoritmo di intelligenza artificiale collega in maniera statistica. Per fornire le risposte, l\u2019algoritmo prende il cammino pi\u00f9 probabile per andare da un frammento ad un altro, a seconda della domanda e considerando il suo contesto. Questo processo non porta sempre a un risultato predicibile, come capita […]<\/p>\n","protected":false},"author":13,"featured_media":1154,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"2880","footnotes":"","_links_to":"","_links_to_target":""},"categories":[1],"tags":[],"class_list":["post-1147","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"yoast_head":"\nCome aumentare l\u2019affidabilit\u00e0 dei chatbot basati su LLM - Interactive Media - Italiano<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/imnet.com\/it\/come-aumentare-laffidabilita-dei-chatbot-basati-su-llm\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Come aumentare l\u2019affidabilit\u00e0 dei chatbot basati su LLM - Interactive Media - Italiano\" \/>\n<meta property=\"og:description\" content=\"ChatGPT e i suoi fratelli vengono usati per sempre pi\u00f9 servizi per il pubblico. \u00c8 affascinante e terrificante nello stesso tempo. 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