{"id":3964,"date":"2023-08-09T06:56:16","date_gmt":"2023-08-09T06:56:16","guid":{"rendered":"https:\/\/dailyai.com\/?p=3964"},"modified":"2023-08-09T06:56:16","modified_gmt":"2023-08-09T06:56:16","slug":"we-want-unbiased-llms-but-its-impossible-heres-why","status":"publish","type":"post","link":"https:\/\/dailyai.com\/it\/2023\/08\/we-want-unbiased-llms-but-its-impossible-heres-why\/","title":{"rendered":"Vogliamo LLM imparziali, ma \u00e8 impossibile. Ecco perch\u00e9."},"content":{"rendered":"

Aziende come OpenAI e Meta stanno lavorando duramente per rendere i loro modelli linguistici pi\u00f9 sicuri e meno distorti, ma i modelli completamente imparziali potrebbero essere un sogno irrealizzabile.<\/strong><\/p>\n

A <\/span>nuovo documento di ricerca<\/span><\/a> dell'Universit\u00e0 di Washington, dell'Universit\u00e0 Carnegie Mellon e dell'Universit\u00e0 Xi'an Jiaotong hanno concluso che tutti i modelli linguistici dell'IA testati mostravano pregiudizi politici.<\/span><\/p>\n

Dopo aver analizzato le fonti del pregiudizio, hanno concluso che il pregiudizio nei modelli linguistici \u00e8 inevitabile.<\/span><\/p>\n

Chan Park, uno degli autori del lavoro, ha dichiarato: \"Crediamo che nessun modello linguistico possa essere completamente libero da pregiudizi politici\".<\/span><\/p>\n

I ricercatori hanno testato 14 diversi modelli linguistici e hanno chiesto loro di esprimere opinioni su argomenti come la democrazia, il razzismo e il femminismo, per vedere da che parte dello spettro politico si collocavano i modelli.<\/span><\/p>\n

I risultati hanno mostrato che ChatGPT e GPT-4 di OpenAI erano pi\u00f9 a sinistra, mentre Llama di Meta ha dato le risposte pi\u00f9 a destra.<\/span><\/p>\n

I dati di addestramento non sono l'unica fonte di distorsioni<\/span><\/h2>\n

L'ovvia fonte di sbieco<\/a> sono i dati su cui questi modelli vengono addestrati. Ma la nuova ricerca ha dimostrato che anche dopo aver ripulito i dati dai pregiudizi, i modelli erano suscettibili di pregiudizi di basso livello che rimanevano nei dati.<\/span><\/p>\n

Ci si aspetterebbe che un LLM addestrato su un gruppo di dati di Fox News sia pi\u00f9 favorevole ai repubblicani nelle sue risposte. Ma il problema non \u00e8 solo nei dati di addestramento.\u00a0<\/span><\/p>\n

\u00c8 emerso che, man mano che i modelli linguistici pre-addestrati vengono messi a punto e utilizzati, acquisiscono ulteriori pregiudizi dai loro operatori.<\/span><\/p>\n

Soroush Vosoughi, professore assistente di informatica al Dartmouth College, ha spiegato che i pregiudizi vengono introdotti in quasi tutte le fasi di sviluppo di un LLM.<\/span><\/p>\n

Un esempio \u00e8 il modo in cui OpenAI cerca di eliminare i pregiudizi dai suoi modelli. Per addestrare i suoi modelli utilizza una tecnica chiamata \"Reinforcement Learning through Human Feedback\" o RLHF.<\/span><\/p>\n

In RLHF un operatore umano addestra il modello in modo simile a come si addestra un cucciolo. Se il cucciolo fa qualcosa di buono riceve un premio. Se mastica le pantofole, \"Cane cattivo!\".<\/span><\/p>\n

Un operatore RLHF pone al modello alcune domande e un altro operatore valuta le risposte multiple che il modello d\u00e0. Il secondo operatore valuta le risposte e le classifica in base a quella che gli \u00e8 piaciuta di pi\u00f9.<\/span><\/p>\n

In un <\/span>su come addestra la sua IA<\/span><\/a>OpenAI ha dichiarato di aver istruito i formatori umani a \"evitare di prendere posizione su argomenti controversi\" e che \"i revisori non devono favorire alcun gruppo politico\".<\/span><\/p>\n

Sembra una buona idea, ma anche se ci sforziamo di non esserlo, tutti gli esseri umani sono prevenuti. E questo influenza inevitabilmente la formazione del modello.\u00a0<\/span><\/p>\n

Anche gli autori dell'articolo che abbiamo citato sopra hanno riconosciuto nelle loro conclusioni che i loro pregiudizi potrebbero aver influenzato la loro ricerca.<\/span><\/p>\n

La soluzione potrebbe essere quella di cercare di rendere questi modelli linguistici non eccessivamente cattivi e poi personalizzarli per allinearli ai pregiudizi che le persone hanno. <\/span><\/p>\n

Spesso le persone dicono di volere la verit\u00e0 imparziale, ma poi finiscono per attenersi alla loro fonte di notizie preferita, come Fox o CNN.\u00a0<\/span><\/p>\n

Non siamo sempre d'accordo su cosa sia giusto o sbagliato e questa nuova ricerca sembra dimostrare che nemmeno l'intelligenza artificiale sar\u00e0 in grado di aiutarci a capirlo.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

Aziende come OpenAI e Meta stanno lavorando duramente per rendere i loro modelli linguistici pi\u00f9 sicuri e meno distorti, ma modelli completamente imparziali potrebbero essere un sogno irrealizzabile. Una nuova ricerca condotta dall'Universit\u00e0 di Washington, dalla Carnegie Mellon University e dalla Xi'an Jiaotong University ha concluso che tutti i modelli linguistici di IA testati presentano pregiudizi politici. Dopo aver analizzato le fonti dei pregiudizi, hanno concluso che questi ultimi sono inevitabili nei modelli linguistici. Chan Park, uno degli autori del lavoro, ha dichiarato: \"Crediamo che nessun modello linguistico possa essere del tutto privo di pregiudizi politici\". I ricercatori hanno testato 14 diversi modelli linguistici e hanno chiesto loro di<\/p>","protected":false},"author":6,"featured_media":3979,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[103,213,207,105,91],"class_list":["post-3964","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-ai-debate","tag-bias","tag-llm","tag-machine-learning","tag-policy"],"yoast_head":"\nWe want unbiased LLMs but it\u2019s impossible. 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