{"id":7671,"date":"2023-11-23T15:10:15","date_gmt":"2023-11-23T15:10:15","guid":{"rendered":"https:\/\/dailyai.com\/?p=7671"},"modified":"2023-11-23T15:13:38","modified_gmt":"2023-11-23T15:13:38","slug":"system-2-attention-improves-accuracy-of-llm-responses","status":"publish","type":"post","link":"https:\/\/dailyai.com\/it\/2023\/11\/system-2-attention-improves-accuracy-of-llm-responses\/","title":{"rendered":"Sistema 2 L'attenzione migliora l'accuratezza delle risposte LLM"},"content":{"rendered":"<p><strong>I modelli linguistici di grandi dimensioni (LLM) sono spesso fuorviati da errori o da un contesto irrilevante in un messaggio. I ricercatori di Meta hanno trovato un modo apparentemente semplice per risolvere il problema.<\/strong><\/p>\n<p>Con l'aumentare delle finestre di contesto, i suggerimenti che inseriamo in un LLM possono diventare sempre pi\u00f9 lunghi e dettagliati. I LLM sono diventati pi\u00f9 bravi a cogliere le sfumature o i piccoli dettagli delle nostre richieste, ma a volte questo pu\u00f2 confonderli.<\/p>\n<p>I primi sistemi di apprendimento automatico utilizzavano un approccio di \"attenzione rigida\" che individuava la parte pi\u00f9 rilevante di un input e rispondeva solo a quella. Questo metodo funziona bene quando si cerca di sottotitolare un'immagine, ma male quando si traduce una frase o si risponde a una domanda a pi\u00f9 livelli.<\/p>\n<p>La maggior parte dei LLM utilizza oggi un approccio di \"attenzione morbida\", che tokenizza l'intero messaggio e assegna pesi a ciascuno di essi.<\/p>\n<p>Meta propone un approccio chiamato <a href=\"https:\/\/arxiv.org\/pdf\/2311.11829.pdf\" target=\"_blank\" rel=\"noopener\">Sistema 2 Attenzione<\/a> (S2A) per ottenere il meglio di entrambi i mondi. S2A sfrutta la capacit\u00e0 di elaborazione del linguaggio naturale di un LLM per prendere le vostre richieste ed eliminare i pregiudizi e le informazioni irrilevanti prima di iniziare a lavorare su una risposta.<\/p>\n<p>Ecco un esempio.<\/p>\n<figure id=\"attachment_7673\" aria-describedby=\"caption-attachment-7673\" style=\"width: 1200px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-7673 size-full\" src=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-Math-example.png\" alt=\"\" width=\"1200\" height=\"750\" srcset=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-Math-example.png 1200w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-Math-example-300x188.png 300w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-Math-example-1024x640.png 1024w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-Math-example-768x480.png 768w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-Math-example-370x231.png 370w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-Math-example-800x500.png 800w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-Math-example-20x13.png 20w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-Math-example-740x463.png 740w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-Math-example-77x48.png 77w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><figcaption id=\"caption-attachment-7673\" class=\"wp-caption-text\">Esempio di S2A Math. Fonte: arXiv<\/figcaption><\/figure>\n<p>S2A elimina le informazioni relative a Max in quanto irrilevanti per la domanda. S2A rigenera un prompt ottimizzato prima di iniziare a lavorarci. I LLM sono notoriamente pessimi a <a href=\"https:\/\/dailyai.com\/it\/2023\/10\/chatgpts-accounting-skills-are-put-to-the-test\/\">matematica<\/a> quindi rendere il prompt meno confuso \u00e8 di grande aiuto.<\/p>\n<p>I LLM sono persone che piacciono e sono felici di essere d'accordo con voi, anche quando avete torto. S2A elimina qualsiasi pregiudizio in una richiesta e poi elabora solo le parti rilevanti della richiesta. In questo modo si riduce quella che i ricercatori di IA chiamano \"sicofanzia\", ovvero la propensione di un modello di IA a leccare il sedere.<\/p>\n<figure id=\"attachment_7674\" aria-describedby=\"caption-attachment-7674\" style=\"width: 1190px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-7674 size-full\" src=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-sycophancy-reduction.png\" alt=\"\" width=\"1190\" height=\"584\" srcset=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-sycophancy-reduction.png 1190w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-sycophancy-reduction-300x147.png 300w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-sycophancy-reduction-1024x503.png 1024w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-sycophancy-reduction-768x377.png 768w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-sycophancy-reduction-370x182.png 370w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-sycophancy-reduction-800x393.png 800w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-sycophancy-reduction-740x363.png 740w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-sycophancy-reduction-20x10.png 20w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-sycophancy-reduction-98x48.png 98w\" sizes=\"auto, (max-width: 1190px) 100vw, 1190px\" \/><figcaption id=\"caption-attachment-7674\" class=\"wp-caption-text\">Riduzione della sicofanzia S2A. Fonte: arXiv<\/figcaption><\/figure>\n<p>S2A \u00e8 in realt\u00e0 solo un prompt di sistema che istruisce il LLM a perfezionare un po' il prompt originale prima di mettersi al lavoro. I risultati ottenuti dai ricercatori con le domande di matematica, di fatti e di forma lunga sono stati impressionanti.<\/p>\n<p>A titolo di esempio, ecco i miglioramenti ottenuti da S2A nelle domande sui fatti. La linea di base era costituita dalle risposte a domande che contenevano pregiudizi, mentre il prompt di Oracle era un prompt ideale raffinato dall'uomo.<\/p>\n<p>S2A si avvicina molto ai risultati del prompt Oracle e offre un miglioramento della precisione di quasi 50% rispetto al prompt di base.<\/p>\n<figure id=\"attachment_7675\" aria-describedby=\"caption-attachment-7675\" style=\"width: 586px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-7675 size-full\" src=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-Comparison-of-results.png\" alt=\"\" width=\"586\" height=\"342\" srcset=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-Comparison-of-results.png 586w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-Comparison-of-results-300x175.png 300w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-Comparison-of-results-370x216.png 370w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-Comparison-of-results-20x12.png 20w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/S2A-Comparison-of-results-82x48.png 82w\" sizes=\"auto, (max-width: 586px) 100vw, 586px\" \/><figcaption id=\"caption-attachment-7675\" class=\"wp-caption-text\">S2A Confronto dei risultati. Fonte: arXiv<\/figcaption><\/figure>\n<p>Qual \u00e8 il problema? La pre-elaborazione del prompt originale prima di rispondere aggiunge ulteriori requisiti di calcolo al processo. Se la richiesta \u00e8 lunga e contiene molte informazioni rilevanti, la rigenerazione della richiesta pu\u00f2 comportare costi significativi.<\/p>\n<p>\u00c8 improbabile che gli utenti migliorino nella scrittura di suggerimenti ben fatti, quindi S2A pu\u00f2 essere un buon modo per aggirare questo problema.<\/p>\n<p>Meta inserir\u00e0 S2A nel suo <a href=\"https:\/\/dailyai.com\/it\/2023\/07\/meta-and-microsoft-release-advanced-ai-llama-2-for-free\/\">Lama<\/a> modello? Non lo sappiamo, ma potete sfruttare voi stessi l'approccio S2A.<\/p>\n<p>Se si fa attenzione a non esprimere opinioni o a non dare suggerimenti di primo piano, \u00e8 pi\u00f9 probabile ottenere risposte accurate da questi modelli.<\/p>","protected":false},"excerpt":{"rendered":"<p>I modelli linguistici di grandi dimensioni (LLM) sono spesso fuorviati da errori o da un contesto irrilevante in un messaggio. I ricercatori di Meta hanno trovato un modo apparentemente semplice per risolvere questo problema. Man mano che le finestre di contesto aumentano, i messaggi che inseriamo in un LLM possono diventare sempre pi\u00f9 lunghi e dettagliati. I LLM sono diventati pi\u00f9 bravi a cogliere le sfumature o i dettagli pi\u00f9 piccoli nei nostri messaggi, ma a volte questo pu\u00f2 confonderli. Le prime macchine per l'apprendimento automatico utilizzavano un approccio di \"attenzione rigida\" che individuava la parte pi\u00f9 rilevante di un input e rispondeva solo a quella. Questo metodo funziona bene quando si sta cercando di inserire una didascalia in un'immagine,<\/p>","protected":false},"author":6,"featured_media":7676,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[118,131],"class_list":["post-7671","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-llms","tag-meta"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>System 2 Attention improves accuracy of LLM responses | 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\/2023\/11\/system-2-attention-improves-accuracy-of-llm-responses\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"System 2 Attention improves accuracy of LLM responses | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Large Language Models (LLM) are often mislead by bias or irrelevant context in a prompt. Researchers at Meta have found a seemingly simple way to fix that. As context windows increase the prompts that we enter into an LLM can become longer and increasingly detailed. LLMs have become better at picking up on the nuances or smaller details in our prompts, but sometimes this can confuse them. Early machine learning used a \u201chard attention\u201d approach that singled out the most relevant part of an input and responded only to that. This works fine when you\u2019re trying to caption an image,\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/it\/2023\/11\/system-2-attention-improves-accuracy-of-llm-responses\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2023-11-23T15:10:15+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-11-23T15:13:38+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/Simplify.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1000\" \/>\n\t<meta property=\"og:image:height\" content=\"666\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\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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