{"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\/pt\/2023\/08\/we-want-unbiased-llms-but-its-impossible-heres-why\/","title":{"rendered":"Queremos LLMs imparciais, mas isso \u00e9 imposs\u00edvel. Eis porqu\u00ea."},"content":{"rendered":"

Empresas como a OpenAI e a Meta est\u00e3o a trabalhar arduamente para tornar os seus modelos lingu\u00edsticos mais seguros e menos tendenciosos, mas modelos completamente imparciais podem ser um sonho imposs\u00edvel.<\/strong><\/p>\n

A <\/span>novo trabalho de investiga\u00e7\u00e3o<\/span><\/a> da Universidade de Washington, da Universidade Carnegie Mellon e da Universidade Xi'an Jiaotong conclu\u00edram que todos os modelos lingu\u00edsticos de IA que testaram apresentavam preconceitos pol\u00edticos.<\/span><\/p>\n

Depois de se debru\u00e7arem sobre as fontes do enviesamento, conclu\u00edram que o enviesamento nos modelos lingu\u00edsticos era inevit\u00e1vel.<\/span><\/p>\n

Chan Park, um dos autores do artigo, disse: \"Acreditamos que nenhum modelo lingu\u00edstico pode ser totalmente isento de preconceitos pol\u00edticos\".<\/span><\/p>\n

Os investigadores testaram 14 modelos lingu\u00edsticos diferentes e pediram-lhes opini\u00f5es sobre temas como a democracia, o racismo e o feminismo, para ver em que lado do espetro pol\u00edtico os modelos se situavam.<\/span><\/p>\n

Os resultados mostraram que o ChatGPT e o GPT-4 da OpenAI estavam mais \u00e0 esquerda, enquanto o Llama da Meta deu as respostas mais \u00e0 direita.<\/span><\/p>\n

Os dados de treino n\u00e3o s\u00e3o a \u00fanica fonte de enviesamento<\/span><\/h2>\n

A fonte \u00f3bvia de preconceito<\/a> s\u00e3o os dados em que estes modelos s\u00e3o treinados. Mas a nova investiga\u00e7\u00e3o demonstrou que, mesmo depois de eliminar os enviesamentos dos dados, os modelos eram suscept\u00edveis a enviesamentos de baixo n\u00edvel que permaneciam nos dados.<\/span><\/p>\n

Seria de esperar que um LLM que foi treinado com um monte de dados da Fox News fosse mais pr\u00f3-republicano nas suas respostas. Mas o problema n\u00e3o est\u00e1 apenas nos dados de treino.\u00a0<\/span><\/p>\n

Verifica-se que, \u00e0 medida que os modelos lingu\u00edsticos pr\u00e9-treinados s\u00e3o afinados e utilizados, v\u00e3o adquirindo mais preconceitos dos seus operadores.<\/span><\/p>\n

Soroush Vosoughi, professor assistente de inform\u00e1tica no Dartmouth College, explicou que o preconceito \u00e9 introduzido em quase todas as fases do desenvolvimento de um LLM.<\/span><\/p>\n

Um exemplo disto \u00e9 a forma como a OpenAI est\u00e1 a tentar eliminar os preconceitos dos seus modelos. Utiliza uma t\u00e9cnica chamada \"Aprendizagem por Refor\u00e7o atrav\u00e9s de Feedback Humano\" ou RLHF para treinar os seus modelos.<\/span><\/p>\n

Na RLHF, um operador humano treina o modelo de forma semelhante a como se treina um cachorro. Se o cachorro fizer algo de bom, recebe uma recompensa. Se roer os chinelos, \"C\u00e3o mau!\"<\/span><\/p>\n

Um operador de RLHF coloca algumas quest\u00f5es ao modelo e outro operador avalia as m\u00faltiplas respostas dadas pelo modelo. O segundo operador avalia as respostas e classifica-as de acordo com a que mais lhe agradou.<\/span><\/p>\n

Num <\/span>publica\u00e7\u00e3o sobre a forma como treina a sua IA<\/span><\/a>A OpenAI afirmou que d\u00e1 instru\u00e7\u00f5es aos formadores humanos para \"evitarem tomar posi\u00e7\u00e3o sobre temas controversos\" e que \"os revisores n\u00e3o devem favorecer nenhum grupo pol\u00edtico\".<\/span><\/p>\n

Parece uma boa ideia, mas mesmo que nos esforcemos muito para n\u00e3o o fazer, todos os humanos s\u00e3o tendenciosos. E isso influencia inevitavelmente o treino do modelo.\u00a0<\/span><\/p>\n

Mesmo os autores do artigo que mencion\u00e1mos acima reconheceram na sua conclus\u00e3o que os seus pr\u00f3prios preconceitos poderiam ter influenciado a sua investiga\u00e7\u00e3o.<\/span><\/p>\n

A solu\u00e7\u00e3o pode passar por tentar fazer com que estes modelos lingu\u00edsticos n\u00e3o sejam notoriamente maus e, depois, personaliz\u00e1-los de modo a que se alinhem com os preconceitos que as pessoas t\u00eam. <\/span><\/p>\n

As pessoas dizem muitas vezes que querem a verdade imparcial, mas acabam por se cingir \u00e0 sua fonte de not\u00edcias preferida, como a Fox ou a CNN.\u00a0<\/span><\/p>\n

Nem sempre estamos de acordo sobre o que est\u00e1 certo ou errado e esta nova investiga\u00e7\u00e3o parece mostrar que a IA tamb\u00e9m n\u00e3o ser\u00e1 capaz de nos ajudar a perceber isso.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

Empresas como a OpenAI e a Meta est\u00e3o a trabalhar arduamente para tornar os seus modelos lingu\u00edsticos mais seguros e menos tendenciosos, mas modelos completamente imparciais podem ser um sonho imposs\u00edvel. Um novo trabalho de investiga\u00e7\u00e3o da Universidade de Washington, da Universidade Carnegie Mellon e da Universidade Xi'an Jiaotong concluiu que todos os modelos lingu\u00edsticos de IA que testaram apresentavam preconceitos pol\u00edticos. Depois de se debru\u00e7arem sobre as fontes da parcialidade, conclu\u00edram que esta \u00e9 inevit\u00e1vel nos modelos lingu\u00edsticos. Chan Park, um dos autores do artigo, afirmou: \"Acreditamos que nenhum modelo lingu\u00edstico pode ser totalmente isento de preconceitos pol\u00edticos\". Os investigadores testaram 14 modelos lingu\u00edsticos diferentes e perguntaram-lhes<\/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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