{"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\/nb\/2024\/01\/llms-improve-when-assuming-gender-neutral-or-male-roles\/","title":{"rendered":"LLM-er blir bedre n\u00e5r de inntar kj\u00f8nnsn\u00f8ytrale eller mannlige roller"},"content":{"rendered":"

Forskere fra University of Michigan fant ut at det \u00e5 be Large Language Models (LLM) om \u00e5 innta kj\u00f8nnsn\u00f8ytrale eller mannlige roller ga bedre svar enn n\u00e5r de brukte kvinnelige roller.<\/strong><\/p>\n

Bruk av systemmeldinger er sv\u00e6rt effektivt n\u00e5r det gjelder \u00e5 forbedre svarene du f\u00e5r fra LLM-er. N\u00e5r du ber ChatGPT om \u00e5 opptre som en \"hjelpsom assistent\", har den en tendens til \u00e5 \u00f8ke innsatsen. Forskerne \u00f8nsket \u00e5 finne ut hvilke sosiale roller som fungerte best, og resultatene deres pekte p\u00e5 p\u00e5g\u00e5ende problemer med skjevheter i AI-modeller.<\/p>\n

Det ville ha v\u00e6rt uoverkommelig \u00e5 kj\u00f8re eksperimentene p\u00e5 ChatGPT, s\u00e5 de brukte \u00e5pen kildekode-modellen FLAN-T5, LLaMA 2<\/a>og OPT-IML.<\/p>\n

For \u00e5 finne ut hvilke roller som var mest nyttige, ba de modellene om \u00e5 innta ulike mellommenneskelige roller, henvende seg til et bestemt publikum eller innta ulike yrkesroller.<\/p>\n

For eksempel kunne de sp\u00f8rre modellen: \"Du er advokat\", \"Du snakker med en far\" eller \"Du snakker med kj\u00e6resten din\".<\/p>\n

Deretter fikk de modellene til \u00e5 svare p\u00e5 2457 sp\u00f8rsm\u00e5l fra Massive Multitask Language Understanding (MMLU)-referansedatasettet, og registrerte n\u00f8yaktigheten i svarene.<\/p>\n

De samlede resultatene publisert i papiret<\/a> viste at \"\u00e5 spesifisere en rolle n\u00e5r man sp\u00f8r, kan effektivt forbedre LLM-enes ytelse med minst 20% sammenlignet med kontrollprompten, der det ikke gis noen kontekst\".<\/p>\n

Da de segmenterte rollene etter kj\u00f8nn, kom den iboende skjevheten i modellene til syne. I alle testene fant de at kj\u00f8nnsn\u00f8ytrale eller mannlige roller presterte bedre enn kvinnelige roller.<\/p>\n

\"\"
Sammenligning av n\u00f8yaktigheten i svarene etter kj\u00f8nnsrolle. Kilde: arXiv<\/figcaption><\/figure>\n

Forskerne fant ingen avgj\u00f8rende \u00e5rsak til kj\u00f8nnsforskjellene, men det kan tyde p\u00e5 at skjevhetene i treningsdatasettene gjenspeiles i modellenes prestasjoner.<\/p>\n

Noen av de andre resultatene de oppn\u00e5dde, ga like mange sp\u00f8rsm\u00e5l som svar. Prompting med en publikumsprompt ga bedre resultater enn prompting med en mellommenneskelig rolle. Med andre ord ga \"Du snakker med en l\u00e6rer\" mer n\u00f8yaktige svar enn \"Du snakker med l\u00e6reren din\".<\/p>\n

Enkelte roller fungerte mye bedre i FLAN-T5 enn i LLaMA 2. \u00c5 be FLAN-T5 om \u00e5 innta rollen som \"politi\" ga gode resultater, men ikke i samme grad i LLaMA 2. Bruk av \"mentor\"- eller \"partner\"-rollene fungerte veldig bra i begge.<\/p>\n

\"\"
De beste sosiale rollene for FLAN-T5 og LLaMA 2. Alternativer med best resultater i r\u00f8dt. Kilde: arXiv<\/figcaption><\/figure>\n

Det er interessant \u00e5 merke seg at rollen som \"hjelpsom assistent\", som fungerer s\u00e5 godt i ChatGPT, havnet et sted mellom 35 og 55 p\u00e5 listen over de beste rollene i resultatene.<\/p>\n

Hvorfor utgj\u00f8r disse subtile forskjellene en forskjell i n\u00f8yaktigheten til utdataene? Vi vet ikke helt, men de gj\u00f8r en forskjell. M\u00e5ten du skriver ledeteksten p\u00e5, og konteksten du oppgir, p\u00e5virker definitivt resultatene du f\u00e5r.<\/p>\n

La oss h\u00e5pe at noen forskere med API-kreditter til overs kan gjenskape denne forskningen ved hjelp av ChatGPT. Det vil v\u00e6re interessant \u00e5 f\u00e5 bekreftet hvilke roller som fungerer best i systemmeldinger for GPT-4. Det er nok en god sjanse for at resultatene vil v\u00e6re skjevt fordelt p\u00e5 kj\u00f8nn, slik de var i denne unders\u00f8kelsen.<\/p>","protected":false},"excerpt":{"rendered":"

Forskere fra University of Michigan fant ut at det \u00e5 be Large Language Models (LLM) om \u00e5 innta kj\u00f8nnsn\u00f8ytrale eller mannlige roller ga bedre svar enn n\u00e5r man brukte kvinnelige roller. Bruk av systeminstruksjoner er sv\u00e6rt effektivt n\u00e5r det gjelder \u00e5 forbedre svarene du f\u00e5r fra LLM-er. N\u00e5r du ber ChatGPT om \u00e5 opptre som en \"hjelpsom assistent\", har den en tendens til \u00e5 forbedre seg. Forskerne \u00f8nsket \u00e5 finne ut hvilke sosiale roller som presterte best, og resultatene deres pekte p\u00e5 p\u00e5g\u00e5ende problemer med skjevheter i AI-modeller. Det ville ha v\u00e6rt uoverkommelig \u00e5 kj\u00f8re eksperimentene p\u00e5 ChatGPT, s\u00e5 de brukte \u00e5pen kildekode-modellene FLAN-T5, LLaMA 2 og OPT-IML. For \u00e5 finne<\/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\/nb\/2024\/01\/llms-improve-when-assuming-gender-neutral-or-male-roles\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\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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