{"id":8817,"date":"2024-01-03T12:07:10","date_gmt":"2024-01-03T12:07:10","guid":{"rendered":"https:\/\/dailyai.com\/?p=8817"},"modified":"2024-01-03T12:10:34","modified_gmt":"2024-01-03T12:10:34","slug":"llms-improve-when-assuming-gender-neutral-or-male-roles","status":"publish","type":"post","link":"https:\/\/dailyai.com\/sv\/2024\/01\/llms-improve-when-assuming-gender-neutral-or-male-roles\/","title":{"rendered":"LLM:er blir b\u00e4ttre n\u00e4r de antar k\u00f6nsneutrala eller manliga roller"},"content":{"rendered":"

Forskare fr\u00e5n University of Michigan fann att n\u00e4r Large Language Models (LLM) uppmanades att anta k\u00f6nsneutrala eller manliga roller gav det b\u00e4ttre svar \u00e4n n\u00e4r kvinnliga roller anv\u00e4ndes.<\/strong><\/p>\n

Att anv\u00e4nda systemprompter \u00e4r mycket effektivt n\u00e4r det g\u00e4ller att f\u00f6rb\u00e4ttra de svar du f\u00e5r fr\u00e5n LLM:er. N\u00e4r du s\u00e4ger till ChatGPT att agera som en \"hj\u00e4lpsam assistent\" tenderar den att h\u00f6ja sitt spel. Forskarna ville uppt\u00e4cka vilka sociala roller som fungerade b\u00e4st och deras resultat pekade p\u00e5 p\u00e5g\u00e5ende problem med partiskhet i AI-modeller.<\/p>\n

Att k\u00f6ra sina experiment p\u00e5 ChatGPT skulle ha varit kostnadsdrivande, s\u00e5 de anv\u00e4nde modeller med \u00f6ppen k\u00e4llkod FLAN-T5, LLaMA 2<\/a>och OPT-IML.<\/p>\n

F\u00f6r att ta reda p\u00e5 vilka roller som var till st\u00f6rst hj\u00e4lp bad man modellerna att anta olika interpersonella roller, v\u00e4nda sig till en specifik publik eller anta olika yrkesroller.<\/p>\n

De kan till exempel fr\u00e5ga modellen: \"Du \u00e4r advokat\", \"Du pratar med en pappa\" eller \"Du pratar med din flickv\u00e4n\".<\/p>\n

De l\u00e4t sedan modellerna svara p\u00e5 2457 fr\u00e5gor fr\u00e5n MMLU:s (Massive Multitask Language Understanding) benchmarkdataset och registrerade svarens noggrannhet.<\/p>\n

De \u00f6vergripande resultaten som publicerades i tidningen<\/a> visade att \"specificering av en roll vid uppmaning effektivt kan f\u00f6rb\u00e4ttra LLM:s prestanda med minst 20% j\u00e4mf\u00f6rt med kontrolluppmaningen, d\u00e4r inget sammanhang ges.\"<\/p>\n

N\u00e4r de delade upp rollerna efter k\u00f6n kom modellernas inneboende partiskhet fram i ljuset. I alla sina tester fann de att k\u00f6nsneutrala eller manliga roller presterade b\u00e4ttre \u00e4n kvinnliga roller.<\/p>\n

\"\"
J\u00e4mf\u00f6relse av svarens noggrannhet beroende p\u00e5 k\u00f6nsroll. K\u00e4lla: arXiv<\/figcaption><\/figure>\n

Forskarna kunde inte ge n\u00e5gon slutgiltig f\u00f6rklaring till k\u00f6nsskillnaderna, men det kan tyda p\u00e5 att f\u00f6rdomar i tr\u00e4ningsdataupps\u00e4ttningarna \u00e5terspeglas i modellernas prestanda.<\/p>\n

N\u00e5gra av de andra resultat som de uppn\u00e5dde v\u00e4ckte lika m\u00e5nga fr\u00e5gor som svar. Att uppmana med en publik uppmaning gav b\u00e4ttre resultat \u00e4n att uppmana med en interpersonell roll. Med andra ord, \"Du pratar med en l\u00e4rare\" gav mer korrekta svar \u00e4n \"Du pratar med din l\u00e4rare\".<\/p>\n

Vissa roller fungerade mycket b\u00e4ttre i FLAN-T5 \u00e4n i LLaMA 2. Att uppmana FLAN-T5 att ta p\u00e5 sig rollen som \"polis\" gav bra resultat, men inte lika bra i LLaMA 2. Att anv\u00e4nda rollerna \"mentor\" eller \"partner\" fungerade riktigt bra i b\u00e5da fallen.<\/p>\n

\"\"
B\u00e4st presterande sociala roller f\u00f6r FLAN-T5 och LLaMA 2. Alternativ med b\u00e4st resultat i r\u00f6tt. K\u00e4lla: arXiv<\/figcaption><\/figure>\n

Intressant nog hamnade rollen som \"hj\u00e4lpsam assistent\", som fungerar s\u00e5 bra i ChatGPT, n\u00e5gonstans mellan 35 och 55 p\u00e5 listan \u00f6ver de b\u00e4sta rollerna i deras resultat.<\/p>\n

Varf\u00f6r g\u00f6r dessa subtila skillnader skillnad i utmatningarnas noggrannhet? Vi vet inte riktigt, men de g\u00f6r skillnad. Hur du skriver din fr\u00e5ga och vilket sammanhang du ger den p\u00e5verkar definitivt vilka resultat du f\u00e5r.<\/p>\n

L\u00e5t oss hoppas att n\u00e5gra forskare med API-krediter att spara kan replikera denna forskning med ChatGPT. Det kommer att bli intressant att f\u00e5 bekr\u00e4ftelse p\u00e5 vilka roller som fungerar b\u00e4st i systemuppmaningar f\u00f6r GPT-4. Det \u00e4r f\u00f6rmodligen en bra satsning att resultaten kommer att vara skeva av k\u00f6n som de var i denna forskning.<\/p>","protected":false},"excerpt":{"rendered":"

Forskare fr\u00e5n University of Michigan fann att om man uppmanade Large Language Models (LLM) att anta k\u00f6nsneutrala eller manliga roller gav det b\u00e4ttre svar \u00e4n n\u00e4r man anv\u00e4nde kvinnliga roller. Att anv\u00e4nda systeminstruktioner \u00e4r mycket effektivt n\u00e4r det g\u00e4ller att f\u00f6rb\u00e4ttra de svar du f\u00e5r fr\u00e5n LLM:er. N\u00e4r du s\u00e4ger till ChatGPT att agera som en \"hj\u00e4lpsam assistent\" tenderar den att h\u00f6ja sitt spel. Forskarna ville uppt\u00e4cka vilka sociala roller som presterade b\u00e4st och deras resultat pekade p\u00e5 p\u00e5g\u00e5ende problem med partiskhet i AI-modeller. Att k\u00f6ra sina experiment p\u00e5 ChatGPT skulle ha varit kostnadsdrivande s\u00e5 de anv\u00e4nde modeller med \u00f6ppen k\u00e4llkod FLAN-T5, LLaMA 2 och OPT-IML. F\u00f6r att hitta<\/p>","protected":false},"author":6,"featured_media":8820,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[118],"class_list":["post-8817","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-llms"],"yoast_head":"\nLLMs improve when assuming gender-neutral or male roles | 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\/sv\/2024\/01\/llms-improve-when-assuming-gender-neutral-or-male-roles\/\" \/>\n<meta property=\"og:locale\" content=\"sv_SE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"LLMs improve when assuming gender-neutral or male roles | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers from the University of Michigan found that prompting Large Language Models (LLM) to assume gender-neutral or male roles elicited better responses than when using female roles. Using system prompts is very effective in improving the responses you get from LLMs. When you tell ChatGPT to act as a \u201chelpful assistant\u201d it tends to up its game. The researchers wanted to discover which social roles performed best and their results pointed to ongoing issues with bias in AI models. Running their experiments on ChatGPT would have been cost-prohibitive so they used open-source models FLAN-T5, LLaMA 2, and OPT-IML. 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