{"id":6851,"date":"2023-10-27T19:21:39","date_gmt":"2023-10-27T19:21:39","guid":{"rendered":"https:\/\/dailyai.com\/?p=6851"},"modified":"2023-10-27T22:55:24","modified_gmt":"2023-10-27T22:55:24","slug":"ai-subjected-to-tests-on-theory-of-mind-and-systematic-generalization","status":"publish","type":"post","link":"https:\/\/dailyai.com\/da\/2023\/10\/ai-subjected-to-tests-on-theory-of-mind-and-systematic-generalization\/","title":{"rendered":"AI udsat for test af Theory of Mind og systematisk generalisering"},"content":{"rendered":"

Forskere har introduceret FANToM, et nyt benchmark designet til grundigt at teste og evaluere store sprogmodellers (LLM'er) forst\u00e5else og anvendelse af Theory of Mind (ToM).<\/b><\/p>\n

Theory of Mind refererer til evnen til at till\u00e6gge sig selv og andre overbevisninger, \u00f8nsker og viden og til at forst\u00e5, at andre har overbevisninger og perspektiver, der er forskellige fra ens egne.\u00a0<\/span><\/p>\n

ToM anses for at v\u00e6re grundlaget for den bevidsthed, som intelligente dyr besidder. Ud over mennesker anses primater som orangutanger, gorillaer og chimpanser for at have ToM, og det samme g\u00e6lder nogle ikke-primater som papeg\u00f8jer og medlemmer af kragefuglefamilien.\u00a0<\/span><\/p>\n

Efterh\u00e5nden som AI-modeller bliver mere komplekse, s\u00f8ger AI-forskere nye metoder til at evaluere evner som ToM.<\/span><\/p>\n

Et nyt benchmark kaldet FANToM<\/span><\/a>som er skabt af forskere fra Allen Institute for AI, University of Washington, Carnegie Mellon University og Seoul National University, uds\u00e6tter maskinl\u00e6ringsmodeller for dynamiske scenarier, der afspejler interaktioner i det virkelige liv.\u00a0<\/span><\/p>\n

Med FANToM g\u00e5r karakterer ind og ud af samtaler, hvilket udfordrer AI-modeller til at opretholde en n\u00f8jagtig forst\u00e5else af, hvem der ved hvad p\u00e5 et givet tidspunkt.<\/span><\/p>\n

Ved at uds\u00e6tte store sprogmodeller (LLM'er) for FANToM viste det sig, at selv de mest avancerede modeller k\u00e6mper med at opretholde en konsistent ToM.\u00a0<\/span><\/p>\n

Modellernes pr\u00e6station var betydeligt lavere end de menneskelige deltageres, hvilket understreger AI's begr\u00e6nsninger i forhold til at forst\u00e5 og navigere i komplekse sociale interaktioner.\u00a0<\/span><\/p>\n

Faktisk dominerede mennesker alle kategorier, som det ses nedenfor.\u00a0<\/span><\/p>\n

\"AI
Mennesker var langt bedre til at svare p\u00e5 ToM-relaterede sp\u00f8rgsm\u00e5l sammenlignet med popul\u00e6re LLM'er. Kilde: FANToM<\/a>.<\/figcaption><\/figure>\n

Et interessant sidepunkt er, at oktober-versionen af GPT-4-modellen blev overg\u00e5et af en tidligere juni-version, hvilket kan underst\u00f8tte de seneste anekdoter blandt brugerne om, at <\/span>ChatGPT bliver v\u00e6rre og v\u00e6rre<\/span><\/a>.\u00a0<\/span><\/p>\n

FANToM afsl\u00f8rede ogs\u00e5 teknikker til at forbedre LLM ToM, som f.eks. chain-of-thought r\u00e6sonnement og andre finjusteringsmetoder. <\/span><\/p>\n

Men kl\u00f8ften mellem AI og menneskelige ToM-f\u00e6rdigheder er stadig stor.\u00a0<\/span><\/p>\n

AI springer mod menneskelignende sprogf\u00e6rdigheder<\/span><\/h2>\n

I en noget relateret, men separat <\/span>unders\u00f8gelse offentliggjort i Nature<\/span><\/a>udviklede forskere et neuralt netv\u00e6rk, der var i stand til at generalisere sprog p\u00e5 samme m\u00e5de som mennesker.\u00a0<\/span><\/p>\n

Dette nye neurale netv\u00e6rk viste en imponerende evne til at integrere nyligt l\u00e6rte ord i sit eksisterende ordforr\u00e5d. Det kunne derefter bruge disse ord i forskellige sammenh\u00e6nge, en kognitiv f\u00e6rdighed kendt som systematisk generalisering.\u00a0<\/span><\/p>\n

Mennesker udviser naturligt systematisk generalisering og inkorporerer problemfrit nyt ordforr\u00e5d i deres repertoire.\u00a0<\/span><\/p>\n

N\u00e5r man f.eks. f\u00f8rst har l\u00e6rt udtrykket \"photobomb\", kan man bruge det i forskellige situationer n\u00e6sten med det samme. Der dukker hele tiden nye slangudtryk op, og mennesker optager dem naturligt i deres ordforr\u00e5d.\u00a0<\/span><\/p>\n

Forskerne udsatte b\u00e5de deres eget brugerdefinerede neurale netv\u00e6rk og ChatGPT for en r\u00e6kke tests og fandt ud af, at ChatGPT haltede bagefter den brugerdefinerede model i ydeevne.<\/span><\/p>\n

Mens LLM'er som ChatGPT udm\u00e6rker sig i mange samtalescenarier, udviser de bem\u00e6rkelsesv\u00e6rdige uoverensstemmelser og huller i andre, et problem, som dette nye neurale netv\u00e6rk l\u00f8ser.<\/span><\/p>\n

For at unders\u00f8ge dette aspekt af sproglig kommunikation udf\u00f8rte forskere et eksperiment med 25 mennesker, hvor de vurderede deres evne til at anvende nyligt l\u00e6rte ord i forskellige sammenh\u00e6nge. <\/span>Fors\u00f8gspersonerne blev introduceret til et pseudosprog best\u00e5ende af nonsensord, der repr\u00e6senterede forskellige handlinger og regler.\u00a0<\/span><\/p>\n

Efter en tr\u00e6ningsfase udm\u00e6rkede deltagerne sig ved at anvende disse abstrakte regler p\u00e5 nye situationer, hvilket viste systematisk generalisering.<\/span><\/p>\n

Da det nyudviklede neurale netv\u00e6rk blev udsat for denne opgave, afspejlede det den menneskelige pr\u00e6station. <\/span>Men da ChatGPT blev udsat for den samme udfordring, havde den store problemer og fejlede mellem 42 og 86% af tiden, afh\u00e6ngigt af den specifikke opgave.<\/span><\/p>\n

Det er vigtigt af to grunde. For det f\u00f8rste kan man argumentere for, at dette nye neurale netv\u00e6rk effektivt udkonkurrerede GPT-4 p\u00e5 denne specifikke opgave - hvilket er imponerende nok. For det andet afsl\u00f8rer denne unders\u00f8gelse nye metoder til at l\u00e6re AI-modeller at generalisere nyt sprog ligesom mennesker.\u00a0<\/span><\/p>\n

Som Elia Bruni, der er specialist i naturlig sprogbehandling ved Osnabr\u00fcck Universitet i Tyskland, beskriver det: \"Det er en stor ting at indf\u00f8re systematik i neurale netv\u00e6rk.\"<\/span><\/p>\n

Tilsammen tilbyder disse to studier nye tilgange til at tr\u00e6ne mere intelligente AI-modeller, der kan konkurrere med mennesker p\u00e5 kritiske omr\u00e5der som lingvistik og Theory of Mind. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

Forskere har introduceret FANToM, et nyt benchmark designet til grundigt at teste og evaluere store sprogmodellers (LLM'er) forst\u00e5else og anvendelse af Theory of Mind (ToM). Theory of Mind henviser til evnen til at till\u00e6gge sig selv og andre overbevisninger, \u00f8nsker og viden og til at forst\u00e5, at andre har overbevisninger og perspektiver, der er forskellige fra ens egne. ToM anses for at v\u00e6re grundlaget for den bevidsthed, som intelligente dyr besidder. Ud over mennesker anses primater som orangutanger, gorillaer og chimpanser for at have ToM, og det samme g\u00e6lder nogle ikke-primater som papeg\u00f8jer og medlemmer af kragefamilien. Som AI-modeller<\/p>","protected":false},"author":2,"featured_media":6853,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[404,115,105,93],"class_list":["post-6851","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-agi","tag-chatgpt","tag-machine-learning","tag-openai"],"yoast_head":"\nAI subjected to tests on Theory of Mind and systematic generalization | DailyAI<\/title>\n<meta name=\"description\" content=\"Researchers have introduced FANToM, a novel benchmark designed to rigorously test and evaluate large language models\u2019 (LLMs) understanding and application of Theory of Mind (ToM).\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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