{"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\/nb\/2023\/08\/we-want-unbiased-llms-but-its-impossible-heres-why\/","title":{"rendered":"Vi vil ha objektive LLM-er, men det er umulig. Her er grunnen til det."},"content":{"rendered":"

Selskaper som OpenAI og Meta jobber hardt for \u00e5 gj\u00f8re spr\u00e5kmodellene sine tryggere og mindre partiske, men helt objektive modeller kan v\u00e6re en \u00f8nskedr\u00f8m.<\/strong><\/p>\n

A <\/span>ny forskningsrapport<\/span><\/a> fra University of Washington, Carnegie Mellon University og Xi'an Jiaotong University konkluderte med at alle AI-spr\u00e5kmodellene de testet, viste politisk slagside.<\/span><\/p>\n

Etter \u00e5 ha unders\u00f8kt kildene til skjevhetene, konkluderte de med at skjevheter i spr\u00e5kmodeller var uunng\u00e5elige.<\/span><\/p>\n

Chan Park, en av artikkelforfatterne, sier: \"Vi tror ikke at noen spr\u00e5kmodell kan v\u00e6re helt fri for politiske skjevheter.\"<\/span><\/p>\n

Forskerne testet 14 ulike spr\u00e5kmodeller og ba dem ta stilling til temaer som demokrati, rasisme og feminisme, for \u00e5 se hvilken side av det politiske spekteret modellene befant seg p\u00e5.<\/span><\/p>\n

Resultatene viste at OpenAIs ChatGPT og GPT-4 l\u00e5 lengst til venstre, mens Metas Llama ga de mest h\u00f8yreorienterte svarene.<\/span><\/p>\n

Treningsdata er ikke den eneste kilden til skjevheter<\/span><\/h2>\n

Den \u00e5penbare kilden til skjevhet<\/a> er dataene disse modellene er trent p\u00e5. Men den nye forskningen viste at selv etter at dataene var renset for skjevheter, var modellene mottakelige for skjevheter p\u00e5 lavt niv\u00e5 som fortsatt fantes i dataene.<\/span><\/p>\n

Man kunne forvente at en LLM som ble trent p\u00e5 en haug med Fox News-data, ville v\u00e6re mer pro-republikansk i sine svar. Men problemet ligger ikke bare i treningsdataene.\u00a0<\/span><\/p>\n

Det viser seg at etter hvert som de forh\u00e5ndstrente spr\u00e5kmodellene finjusteres og brukes, f\u00e5r de ytterligere skjevheter fra operat\u00f8rene sine.<\/span><\/p>\n

Soroush Vosoughi, assisterende professor i informatikk ved Dartmouth College, forklarte at skjevheter introduseres p\u00e5 nesten alle stadier av en LLMs utvikling.<\/span><\/p>\n

Et eksempel p\u00e5 dette er hvordan OpenAI fors\u00f8ker \u00e5 fjerne skjevheter fra modellene sine. De bruker en teknikk som kalles \"Reinforcement Learning through Human Feedback\" (RLHF) for \u00e5 trene opp modellene sine.<\/span><\/p>\n

I RLHF trener en menneskelig operat\u00f8r modellen p\u00e5 samme m\u00e5te som man trener en valp. Hvis valpen gj\u00f8r noe bra, f\u00e5r den en godbit. Hvis den tygger i t\u00f8flene dine, f\u00e5r den \"slem hund!\"<\/span><\/p>\n

En RLHF-operat\u00f8r stiller noen sp\u00f8rsm\u00e5l til modellen, og en annen operat\u00f8r evaluerer deretter de mange svarene modellen gir. Den andre operat\u00f8ren evaluerer svarene og rangerer dem etter hvilke de likte best.<\/span><\/p>\n

I en <\/span>innlegg om hvordan den trener opp sin AI<\/span><\/a>OpenAI sier at de instruerer menneskelige trenere om \u00e5 \"unng\u00e5 \u00e5 ta stilling til kontroversielle temaer\" og at \"anmeldere ikke b\u00f8r favorisere noen politisk gruppe\".<\/span><\/p>\n

Dette h\u00f8res ut som en god id\u00e9, men selv om vi pr\u00f8ver hardt \u00e5 ikke v\u00e6re det, er alle mennesker forutinntatte. Og det p\u00e5virker uunng\u00e5elig modellens oppl\u00e6ring.\u00a0<\/span><\/p>\n

Selv forfatterne av artikkelen vi nevnte ovenfor, erkjente i sin konklusjon at deres egen forutinntatthet kunne ha p\u00e5virket forskningen deres.<\/span><\/p>\n

L\u00f8sningen kan v\u00e6re \u00e5 fors\u00f8ke \u00e5 lage spr\u00e5kmodeller som ikke er helt d\u00e5rlige, og deretter tilpasse dem til de fordommene folk har. <\/span><\/p>\n

Folk sier ofte at de vil ha den objektive sannheten, men s\u00e5 ender de opp med \u00e5 holde seg til sin foretrukne nyhetskilde som Fox eller CNN.\u00a0<\/span><\/p>\n

Vi er ikke alltid enige om hva som er riktig eller galt, og denne nye forskningen ser ut til \u00e5 vise at AI heller ikke vil kunne hjelpe oss med \u00e5 finne ut av det.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

Selskaper som OpenAI og Meta jobber hardt for \u00e5 gj\u00f8re spr\u00e5kmodellene sine tryggere og mindre partiske, men helt objektive modeller kan v\u00e6re en \u00f8nskedr\u00f8m. En ny forskningsrapport fra University of Washington, Carnegie Mellon University og Xi'an Jiaotong University konkluderte med at alle AI-spr\u00e5kmodellene de testet, viste politiske skjevheter. Etter \u00e5 ha unders\u00f8kt kildene til skjevhetene, konkluderte de med at skjevheter i spr\u00e5kmodeller var uunng\u00e5elige. Chan Park, en av forfatterne av artikkelen, sier: \"Vi tror ikke at noen spr\u00e5kmodell kan v\u00e6re helt fri for politiske skjevheter.\" Forskerne testet 14 ulike spr\u00e5kmodeller og spurte dem<\/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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