{"id":2163,"date":"2023-06-30T21:15:16","date_gmt":"2023-06-30T21:15:16","guid":{"rendered":"https:\/\/dailyai.com\/?p=2163"},"modified":"2023-07-03T16:28:22","modified_gmt":"2023-07-03T16:28:22","slug":"anthropic-releases-paper-highlighting-the-potential-bias-of-large-language-models","status":"publish","type":"post","link":"https:\/\/dailyai.com\/pt\/2023\/06\/anthropic-releases-paper-highlighting-the-potential-bias-of-large-language-models\/","title":{"rendered":"O documento Anthropic releases revela o enviesamento dos modelos lingu\u00edsticos de grande dimens\u00e3o"},"content":{"rendered":"

Um novo artigo da empresa Anthropic, especializada em intelig\u00eancia artificial, revelou os potenciais preconceitos inerentes aos modelos de linguagem de grande dimens\u00e3o (LLM), sugerindo que estes sistemas de IA podem n\u00e3o representar adequadamente as diversas perspectivas globais sobre quest\u00f5es sociais.<\/strong><\/p>\n

Os investigadores criaram um conjunto de dados, GlobalOpinionQA, que inclui perguntas e respostas de inqu\u00e9ritos transnacionais concebidos para captar opini\u00f5es variadas sobre quest\u00f5es globais em diferentes pa\u00edses.\u00a0<\/span><\/p>\n

Antr\u00f3pico experi\u00eancias<\/span><\/a> questionou um LLM e descobriu que, por defeito, as respostas do modelo tendiam a alinhar-se mais com as opini\u00f5es de popula\u00e7\u00f5es espec\u00edficas, nomeadamente as dos EUA, Reino Unido, Canad\u00e1, Austr\u00e1lia e alguns outros pa\u00edses europeus e sul-americanos.\u00a0<\/span><\/p>\n

Como funciona<\/span><\/h2>\n
    \n
  1. Cria\u00e7\u00e3o de conjuntos de dados<\/b>: A equipa criou o conjunto de dados GlobalOpinionQA. Este conjunto de dados incorpora perguntas e respostas de inqu\u00e9ritos transnacionais especificamente concebidos para captar uma gama diversificada de opini\u00f5es sobre quest\u00f5es globais.<\/span><\/li>\n
  2. Defini\u00e7\u00e3o de uma m\u00e9trica de similaridade<\/b>: Em seguida, o Anthropic formulou uma m\u00e9trica para medir a semelhan\u00e7a entre as respostas dadas pelos LLMs e as respostas das pessoas. Esta m\u00e9trica tem em conta o pa\u00eds de origem dos inquiridos humanos.<\/span><\/li>\n
  3. Forma\u00e7\u00e3o do LLM<\/b>: O Anthropic formou um LLM com base na \"IA Constitucional\", garantindo que o LLM era \u00fatil, honesto e inofensivo. A IA constitucional \u00e9 uma t\u00e9cnica desenvolvida pela Anthropic que tem como objetivo imbuir os sistemas de IA de \"valores\" definidos por uma \"constitui\u00e7\u00e3o\".<\/span><\/li>\n
  4. Realiza\u00e7\u00e3o de experi\u00eancias<\/b>: Utilizando a sua estrutura cuidadosamente concebida, a equipa da Anthropic executou 3 experi\u00eancias distintas no LLM treinado.<\/span><\/li>\n<\/ol>\n

    Os investigadores argumentam que este facto evidencia um potencial enviesamento nos modelos, levando \u00e0 sub-representa\u00e7\u00e3o das opini\u00f5es de certos grupos em compara\u00e7\u00e3o com as dos pa\u00edses ocidentais.\u00a0<\/span><\/p>\n

    Se um modelo lingu\u00edstico representar desproporcionadamente determinadas opini\u00f5es, corre o risco de impor efeitos potencialmente indesej\u00e1veis, como a promo\u00e7\u00e3o de vis\u00f5es do mundo hegem\u00f3nicas e a homogeneiza\u00e7\u00e3o das perspectivas e cren\u00e7as das pessoas\", referiram.<\/span><\/p>\n

    Al\u00e9m disso, os investigadores observaram que o facto de o modelo considerar a perspetiva de um pa\u00eds espec\u00edfico conduziu a respostas mais semelhantes \u00e0s opini\u00f5es dessas popula\u00e7\u00f5es. <\/span><\/p>\n

    Isto significa que se pode pedir \u00e0 IA que \"considere a perspetiva sul-americana\" num determinado debate cultural, por exemplo. <\/span>No entanto, estas respostas reflectiam, por vezes, estere\u00f3tipos culturais nocivos, sugerindo que os modelos n\u00e3o t\u00eam uma compreens\u00e3o diferenciada dos valores e perspectivas culturais.<\/p>\n

    Curiosamente, quando os investigadores traduziram as perguntas do GlobalOpinionQA para uma l\u00edngua-alvo, as respostas do modelo n\u00e3o corresponderam necessariamente \u00e0s opini\u00f5es dos falantes dessas l\u00ednguas. <\/span><\/p>\n

    Por isso, fazer uma pergunta, por exemplo, em japon\u00eas, n\u00e3o conduz necessariamente a respostas alinhadas com os valores culturais japoneses. N\u00e3o se pode \"separar\" a IA dos seus valores predominantemente ocidentais. <\/span><\/p>\n

    Isto sugere que, apesar da sua adaptabilidade, os LLMs devem adquirir uma compreens\u00e3o mais profunda dos contextos sociais para gerar respostas que reflictam com precis\u00e3o as opini\u00f5es locais.<\/span><\/p>\n

    Os investigadores acreditam que as suas descobertas ir\u00e3o proporcionar transpar\u00eancia nas perspectivas codificadas e reflectidas pelos modelos lingu\u00edsticos actuais. Apesar das limita\u00e7\u00f5es do seu estudo, esperam que este sirva de orienta\u00e7\u00e3o para o desenvolvimento de sistemas de IA que incorporem uma diversidade de pontos de vista e experi\u00eancias culturais, e n\u00e3o apenas os de grupos privilegiados ou dominantes. Tamb\u00e9m publicaram o seu conjunto de dados e um <\/span>visualiza\u00e7\u00e3o interactiva.<\/span><\/a><\/p>\n

    Este estudo est\u00e1 amplamente alinhado com outros trabalhos acad\u00e9micos sobre o tema dos valores sociais e culturais da IA.<\/p>\n

    Por um lado, a maioria das IA fundamentais s\u00e3o treinadas por empresas e equipas de investiga\u00e7\u00e3o predominantemente ocidentais.<\/p>\n

    Al\u00e9m disso, o dados utilizados para treinar IAs<\/a> nem sempre representa a sociedade como um todo. Por exemplo, a grande maioria dos dados de forma\u00e7\u00e3o para os LLM est\u00e1 escrita em ingl\u00eas, reflectindo provavelmente os valores sociais e culturais da l\u00edngua inglesa.<\/p>\n

    Os investigadores est\u00e3o bem cientes dos potenciais preconceitos e discrimina\u00e7\u00e3o na IA. No entanto, a sua resolu\u00e7\u00e3o \u00e9 extremamente complexa, exigindo uma combina\u00e7\u00e3o cuidadosa de conjuntos de dados personalizados de alta qualidade e de um contributo e monitoriza\u00e7\u00e3o humanos diligentes.<\/p>","protected":false},"excerpt":{"rendered":"

    Um novo artigo da empresa de IA Anthropic lan\u00e7ou luz sobre os potenciais enviesamentos inerentes aos modelos de linguagem de grande dimens\u00e3o (LLM), sugerindo que estes sistemas de IA podem n\u00e3o representar adequadamente diversas perspectivas globais sobre quest\u00f5es sociais. Os investigadores criaram um conjunto de dados, GlobalOpinionQA, que inclui perguntas e respostas de inqu\u00e9ritos transnacionais concebidos para captar opini\u00f5es variadas sobre quest\u00f5es globais em diferentes pa\u00edses. As experi\u00eancias do Anthropic questionaram um LLM e descobriram que, por defeito, as respostas do modelo tendiam a alinhar-se mais com as opini\u00f5es de popula\u00e7\u00f5es espec\u00edficas, nomeadamente as dos EUA, Reino Unido, Canad\u00e1, Austr\u00e1lia e alguns outros pa\u00edses europeus e sul-americanos. Como<\/p>","protected":false},"author":2,"featured_media":2164,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[148,118],"class_list":["post-2163","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-anthropic","tag-llms"],"yoast_head":"\nAnthropic releases paper revealing the bias of large language models | DailyAI<\/title>\n<meta name=\"description\" content=\"A new paper by AI company Anthropic has shed light on the potential biases inherent in large language models (LLMs), suggesting these AI systems may not adequately represent diverse global perspectives on societal issues.\" \/>\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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