{"id":10866,"date":"2024-03-22T10:03:11","date_gmt":"2024-03-22T10:03:11","guid":{"rendered":"https:\/\/dailyai.com\/?p=10866"},"modified":"2024-03-28T09:32:30","modified_gmt":"2024-03-28T09:32:30","slug":"quiet-star-teaches-language-models-to-think-before-they-speak","status":"publish","type":"post","link":"https:\/\/dailyai.com\/it\/2024\/03\/quiet-star-teaches-language-models-to-think-before-they-speak\/","title":{"rendered":"Quiet-STaR insegna ai modelli linguistici a pensare prima di parlare"},"content":{"rendered":"<p><strong>I ricercatori della Stanford University e di Notbad AI hanno sviluppato Quiet-STaR, una tecnica che addestra un modello linguistico (LM) a ragionare internamente prima di generare un output.<\/strong><\/p>\n<p>Quando gli esseri umani parlano, normalmente hanno un dialogo interiore che d\u00e0 forma alle parole che poi verbalizziamo. Pi\u00f9 pensiamo prima di parlare, migliore sar\u00e0 la qualit\u00e0 delle nostre parole.<\/p>\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2403.09629.pdf\" target=\"_blank\" rel=\"noopener\">Nel loro documento<\/a>, i ricercatori descrivono come hanno addestrato un LM (<a href=\"https:\/\/dailyai.com\/it\/2024\/02\/mistral-ai-releases-new-model-and-chatbot-to-take-on-gpt-4\/\">Mistral-7B<\/a>) per imparare a imitare questo processo in modo generalizzato. Quiet-STaR \u00e8 una progressione di un'altra tecnica chiamata STaR, o Self-Taught Reasoner.<\/p>\n<p>STaR \u00e8 un metodo per addestrare un modello con alcuni esempi di domande con spiegazioni (razionali) per le risposte. Il modello utilizza questi esempi di catena di pensiero per provare a rispondere alle domande da solo, comprendendo i razionali.<\/p>\n<p>STaR valuta se i razionali da lui proposti portano a risposte corrette e affina i suoi razionali.<\/p>\n<p>Per quanto STaR sia impressionante, la sua capacit\u00e0 di ragionare \u00e8 limitata ai contesti di risposta alle domande (QA) durante l'addestramento. L'obiettivo di Quiet-STaR \u00e8 fornire a un LM una capacit\u00e0 generalizzata di imparare a ragionare o a sviluppare razionali, su una gamma pi\u00f9 ampia di testi, non solo su insiemi di dati QA.<\/p>\n<h2>Come funziona Quiet-STaR?<\/h2>\n<blockquote class=\"twitter-tweet\">\n<p dir=\"ltr\" lang=\"en\">Oggi i modelli linguistici sono addestrati a ragionare 1) in modo generale, imitando i dati di ragionamento online, oppure 2) in modo ristretto, autoapprendendo le proprie soluzioni a compiti specifici.<\/p>\n<p>I LM possono insegnare a ragionare in generale? \ud83c\udf1f Introduciamo Quiet-STaR, l'autoapprendimento tramite monologo interno! <a href=\"https:\/\/t.co\/WCSxLPZeCX\">pic.twitter.com\/WCSxLPZeCX<\/a><\/p>\n<p>- Eric Zelikman (@ericzelikman) <a href=\"https:\/\/twitter.com\/ericzelikman\/status\/1768663835106513041?ref_src=twsrc%5Etfw\">15 marzo 2024<\/a><\/p><\/blockquote>\n<p><script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/p>\n<p>Una delle innovazioni principali di Quiet-STaR \u00e8 che genera razionali, o pensieri, in parallelo, seguendo tutti i token del testo che sta elaborando. Non produce questi ragionamenti a catena, da cui il nome \"Quiet\" dell'algoritmo.<\/p>\n<p>L'algoritmo elabora i razionali attraverso una \"testa di miscelazione\". Ogni razionale viene valutato in base all'accuratezza della previsione del token successivo che ha prodotto rispetto alla previsione fatta dal modello di base.<\/p>\n<p>Se il modello di base (senza Quiet-STaR) fornisce una previsione migliore, allora la motivazione non era buona. Se la logica risulta in una previsione pi\u00f9 accurata del prossimo token, allora l'algoritmo sa di essere sulla buona strada.<\/p>\n<p>Utilizza poi un algoritmo di apprendimento per rinforzo (REINFORCE) per imparare quali razionali aiutano e quali ostacolano le prestazioni del modello. Il risultato \u00e8 che il modello apprende una capacit\u00e0 generalizzata di pensare prima di prevedere il token successivo.<\/p>\n<h2>Risultati di Quiet-STaR<\/h2>\n<p>I ricercatori hanno testato il modello Mistral-7B addestrato da Quiet-STaR sui benchmark di ragionamento matematico GSM8K e di senso comune CommonsenseQA. Hanno scoperto che Quiet-STaR ha migliorato la perplessit\u00e0 e le capacit\u00e0 di ragionamento diretto a zero colpi sia su CommonsenseQA (da 36,3% a 47,2%) sia su GSM8K (da 5,9% a 10,9%).<\/p>\n<figure id=\"attachment_10868\" aria-describedby=\"caption-attachment-10868\" style=\"width: 1334px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-10868\" src=\"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/03\/Quiet-STaR-benchmark-results.jpg\" alt=\"\" width=\"1334\" height=\"518\" srcset=\"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/03\/Quiet-STaR-benchmark-results.jpg 1334w, https:\/\/dailyai.com\/wp-content\/uploads\/2024\/03\/Quiet-STaR-benchmark-results-300x116.jpg 300w, https:\/\/dailyai.com\/wp-content\/uploads\/2024\/03\/Quiet-STaR-benchmark-results-1024x398.jpg 1024w, https:\/\/dailyai.com\/wp-content\/uploads\/2024\/03\/Quiet-STaR-benchmark-results-768x298.jpg 768w, https:\/\/dailyai.com\/wp-content\/uploads\/2024\/03\/Quiet-STaR-benchmark-results-370x144.jpg 370w, https:\/\/dailyai.com\/wp-content\/uploads\/2024\/03\/Quiet-STaR-benchmark-results-800x311.jpg 800w, https:\/\/dailyai.com\/wp-content\/uploads\/2024\/03\/Quiet-STaR-benchmark-results-740x287.jpg 740w, https:\/\/dailyai.com\/wp-content\/uploads\/2024\/03\/Quiet-STaR-benchmark-results-20x8.jpg 20w, https:\/\/dailyai.com\/wp-content\/uploads\/2024\/03\/Quiet-STaR-benchmark-results-124x48.jpg 124w\" sizes=\"auto, (max-width: 1334px) 100vw, 1334px\" \/><figcaption id=\"caption-attachment-10868\" class=\"wp-caption-text\">Risultati di Quiet-STaR sui benchmark GMSK8 per la matematica e CommonsenseQA per il ragionamento di senso comune. Ogni riga rappresenta un'iterazione di Quiet-STaR con una lunghezza variabile dei token di pensiero e quanti token in avanti ha ragionato. La linea di base \u00e8 Mistral-7B senza Quiet-STaR. Fonte: arXiv<\/figcaption><\/figure>\n<p>Sebbene il ragionamento matematico di Mistral-7B non sia ancora eccezionale, Quiet-STaR ha fornito un miglioramento di quasi 85% rispetto al modello base, e questo senza alcuna messa a punto specifica del dataset\".<\/p>\n<p>I risultati dei test hanno anche mostrato che i miglioramenti delle prestazioni erano direttamente correlati al numero di gettoni assegnati ai pensieri interni del modello. Pi\u00f9 il modello pensava prima di rispondere, migliore era la risposta.<\/p>\n<p>Questi miglioramenti comportano un notevole sovraccarico di calcolo. Il monologo interiore del modello durante il processo di pensiero genera molti token.<\/p>\n<p>I miglioramenti dell'hardware finiranno per rendere meno rilevante l'overhead aggiuntivo che deriva da tecniche come queste.<\/p>\n<p>I ricercatori concludono che anche il lavoro futuro sull'ottimizzazione di Quiet-STaR potrebbe essere utile. La previsione dinamica della necessit\u00e0 di un processo di pensiero o della sua durata potrebbe ridurre i token di pensiero non necessari.<\/p>\n<p>I risultati dell'addestramento di un modello piccolo come Mistral-7B con Quiet-STaR sono promettenti. I ricercatori ritengono che \"le stesse tecniche applicate a un modello migliore darebbero probabilmente risultati sproporzionatamente migliori\".<\/p>\n<h2>Questioni etiche<\/h2>\n<p>Far ragionare un modello linguistico pi\u00f9 simile a un essere umano comporta alcuni problemi interessanti e questioni etiche.<\/p>\n<p>I ricercatori osservano che \"\u00e8 impossibile sapere che il ragionamento espresso dal modello nel linguaggio rappresenti accuratamente l'elaborazione interna del modello\". I razionali generati dal modello sono rappresentazioni in linguaggio naturale del suo ragionamento interno. Sono un riflesso accurato?<\/p>\n<p>Inoltre, notano che \"non ci sono salvaguardie contro modelli di ragionamento dannosi o distorti, se il modello li trova utili\".<\/p>\n<p>Potremmo essere soddisfatti della risposta di un modello di intelligenza artificiale, ma potrebbe non piacerci, o non capire, il processo di pensiero che l'ha prodotta.<\/p>\n<p>Uno degli autori principali del documento, Eric Zelikman, si \u00e8 appena unito questa settimana alla xAI di Elon Musk. Potrebbe scoprire che <a href=\"https:\/\/dailyai.com\/it\/2024\/03\/elon-musks-xai-open-sources-its-llm-grok-1\/\">Grok<\/a> \u00e8 meno preoccupato da queste questioni etiche e pi\u00f9 eccitato dalla prospettiva del progresso dell'IA.<\/p>\n<p>&nbsp;<\/p>","protected":false},"excerpt":{"rendered":"<p>I ricercatori della Stanford University e di Notbad AI hanno sviluppato Quiet-STaR, una tecnica che addestra un modello linguistico (LM) a ragionare internamente prima di generare un output. Quando gli esseri umani parlano, normalmente hanno un dialogo interiore che d\u00e0 forma alle parole che alla fine verbalizziamo. Pi\u00f9 pensiamo prima di parlare, migliore sar\u00e0 la qualit\u00e0 delle nostre parole. Nel loro articolo, i ricercatori descrivono come hanno addestrato un LM (Mistral-7B) a imparare a imitare questo processo in modo generalizzato. Quiet-STaR \u00e8 una progressione di un'altra tecnica chiamata STaR, o Self-Taught Reasoner. STaR \u00e8 un metodo di addestramento di un modello con pochi<\/p>","protected":false},"author":6,"featured_media":10869,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[118],"class_list":["post-10866","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-llms"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Quiet-STaR teaches language models to think before they speak | DailyAI<\/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:\/\/dailyai.com\/it\/2024\/03\/quiet-star-teaches-language-models-to-think-before-they-speak\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Quiet-STaR teaches language models to think before they speak | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers from Stanford University and Notbad AI developed Quiet-STaR, a technique that trains a language model (LM) to reason internally before generating an output. When humans speak, we normally have an inner dialogue that shapes the words we eventually verbalize. The more we think before speaking, the better the quality of our spoken words. In their paper, the researchers describe how they trained an LM (Mistral-7B) to learn how to imitate this process in a generalized way. Quiet-STaR is a progression of another technique called STaR, or Self-Taught Reasoner. STaR is a method of training a model with a few\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/it\/2024\/03\/quiet-star-teaches-language-models-to-think-before-they-speak\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2024-03-22T10:03:11+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-03-28T09:32:30+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/03\/the-thinker.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1792\" \/>\n\t<meta property=\"og:image:height\" content=\"1024\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Eugene van der Watt\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DailyAIOfficial\" \/>\n<meta name=\"twitter:site\" content=\"@DailyAIOfficial\" \/>\n<meta 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