{"id":11410,"date":"2024-04-08T10:48:47","date_gmt":"2024-04-08T10:48:47","guid":{"rendered":"https:\/\/dailyai.com\/?p=11410"},"modified":"2024-04-08T10:48:47","modified_gmt":"2024-04-08T10:48:47","slug":"claude-3-opus-blows-all-llms-away-in-book-length-summarization","status":"publish","type":"post","link":"https:\/\/dailyai.com\/pt\/2024\/04\/claude-3-opus-blows-all-llms-away-in-book-length-summarization\/","title":{"rendered":"Claude 3 Opus surpreende todos os LLMs com o resumo de um livro"},"content":{"rendered":"

Os investigadores publicaram um estudo que compara a exatid\u00e3o e a qualidade dos resumos que os LLM produzem. O Claude 3 Opus teve um desempenho particularmente bom, mas os humanos continuam a ter vantagem.<\/strong><\/p>\n

Os modelos de IA s\u00e3o extremamente \u00fateis para resumir documentos longos quando n\u00e3o se tem tempo ou vontade de os ler.<\/p>\n

O luxo de aumentar as janelas de contexto significa que podemos solicitar aos modelos documentos mais longos, o que desafia a sua capacidade de obter sempre os factos correctos no resumo.<\/p>\n

Os investigadores da Universidade de Massachusetts Amherst, da Adobe, do Allen Institute for AI e da Universidade de Princeton, publicou um estudo<\/a> que procurou descobrir a capacidade dos modelos de IA para resumir conte\u00fados de livros (>100k tokens).<\/p>\n

FABULOS<\/h2>\n

Seleccionaram 26 livros publicados em 2023 e 2024 e fizeram com que v\u00e1rios LLMs resumissem os textos. As datas de publica\u00e7\u00e3o recentes foram escolhidas para evitar a potencial contamina\u00e7\u00e3o de dados nos dados de treino originais dos modelos.<\/p>\n

Depois de os modelos produzirem os resumos, utilizaram o GPT-4 para extrair deles afirma\u00e7\u00f5es descontextualizadas. Em seguida, os investigadores contrataram anotadores humanos que tinham lido os livros e pediram-lhes que verificassem as afirma\u00e7\u00f5es.<\/p>\n

\"\"
O LLM resume o livro, o GPT-4 extrai as afirma\u00e7\u00f5es e os anotadores humanos verificam as afirma\u00e7\u00f5es. Fonte: arXiv<\/figcaption><\/figure>\n

Os dados resultantes foram compilados num conjunto de dados denominado \"Faithfulness Annotations for Book-Length Summarization\" (FABLES). O FABLES cont\u00e9m 3.158 anota\u00e7\u00f5es de fidelidade ao n\u00edvel da afirma\u00e7\u00e3o em 26 textos narrativos.<\/p>\n

Os resultados do teste mostraram que o Claude 3 Opus era \"o resumidor de livros mais fiel por uma margem significativa\", com mais de 90% das suas afirma\u00e7\u00f5es verificadas como fi\u00e9is ou exactas.<\/p>\n

O GPT-4 ficou num distante segundo lugar, com apenas 78% das suas afirma\u00e7\u00f5es verificadas como fi\u00e9is pelos anotadores humanos.<\/p>\n

\"\"
Percentagem de afirma\u00e7\u00f5es extra\u00eddas de resumos gerados por LLM classificadas por humanos como fi\u00e9is, infi\u00e9is, apoio parcial ou n\u00e3o podem verificar. Fonte: arXiv<\/figcaption><\/figure>\n

A parte dif\u00edcil<\/h2>\n

Os modelos testados pareciam todos ter dificuldades com as mesmas coisas. A maioria dos factos que os modelos erraram diziam respeito a acontecimentos ou estados de personagens e rela\u00e7\u00f5es.<\/p>\n

O documento refere que \"a maior parte destas afirma\u00e7\u00f5es s\u00f3 pode ser invalidada atrav\u00e9s de um racioc\u00ednio multi-hop sobre as provas, o que real\u00e7a a complexidade da tarefa e a sua diferen\u00e7a em rela\u00e7\u00e3o aos cen\u00e1rios de verifica\u00e7\u00e3o de factos existentes\".<\/p>\n

Os LLMs tamb\u00e9m deixaram frequentemente de fora informa\u00e7\u00e3o cr\u00edtica nos seus resumos. Al\u00e9m disso, d\u00e3o demasiada import\u00e2ncia aos conte\u00fados do final dos livros, esquecendo conte\u00fados importantes mais pr\u00f3ximos do in\u00edcio.<\/p>\n

Ir\u00e1 a IA substituir os anotadores humanos?<\/h2>\n

Os anotadores humanos ou verificadores de factos s\u00e3o dispendiosos. Os investigadores gastaram $5.200 para que os anotadores humanos verificassem as afirma\u00e7\u00f5es nos resumos da IA.<\/p>\n

Poderia um modelo de IA ter feito o trabalho por menos? Simples recupera\u00e7\u00e3o de factos<\/a> \u00e9 algo em que o Claude 3 \u00e9 bom, mas o seu desempenho na verifica\u00e7\u00e3o de afirma\u00e7\u00f5es que exigem uma compreens\u00e3o mais profunda do conte\u00fado \u00e9 menos consistente.<\/p>\n

Quando lhes foram apresentadas as afirma\u00e7\u00f5es extra\u00eddas e lhes foi pedido que as verificassem, todos os modelos de IA ficaram aqu\u00e9m dos anotadores humanos. O seu desempenho foi particularmente mau na identifica\u00e7\u00e3o de afirma\u00e7\u00f5es infi\u00e9is.<\/p>\n

Apesar de o Claude 3 Opus ter sido o melhor verificador de reclama\u00e7\u00f5es por alguma dist\u00e2ncia, os investigadores conclu\u00edram que \"acaba por ter um desempenho demasiado fraco para ser um avaliador autom\u00e1tico fi\u00e1vel\".<\/p>\n

Quando se trata de compreender as nuances, as rela\u00e7\u00f5es humanas complexas, os pontos de enredo e as motiva\u00e7\u00f5es das personagens numa longa narrativa, parece que os humanos ainda est\u00e3o em vantagem, por enquanto.<\/p>","protected":false},"excerpt":{"rendered":"

Os investigadores publicaram um estudo que compara a exatid\u00e3o e a qualidade dos resumos que os LLM produzem. O Claude 3 Opus teve um desempenho particularmente bom, mas os humanos continuam a ter vantagem. Os modelos de IA s\u00e3o extremamente \u00fateis para resumir documentos longos quando n\u00e3o se tem tempo ou vontade de os ler. O luxo de janelas de contexto crescentes significa que os modelos podem ser solicitados com documentos mais longos, o que p\u00f5e em causa a sua capacidade de resumir sempre os factos. Os investigadores da Universidade de Massachusetts Amherst, da Adobe, do Allen Institute for AI e da Universidade de Princeton publicaram um estudo que procurava descobrir como<\/p>","protected":false},"author":6,"featured_media":11415,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[118],"class_list":["post-11410","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-llms"],"yoast_head":"\nClaude 3 Opus blows all LLMs away in book-length summarization | 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\/pt\/2024\/04\/claude-3-opus-blows-all-llms-away-in-book-length-summarization\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Claude 3 Opus blows all LLMs away in book-length summarization | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers published a study comparing the accuracy and quality of summaries that LLMs produce. Claude 3 Opus performed particularly well but humans still have the edge. AI models are extremely useful for summarizing long documents when you don\u2019t have the time or inclination to read them. The luxury of growing context windows means we get to prompt models with longer documents, which challenges their ability to always get the facts straight in the summary. 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