{"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\/de\/2024\/04\/claude-3-opus-blows-all-llms-away-in-book-length-summarization\/","title":{"rendered":"Claude 3 Opus \u00fcbertrifft alle LLMs bei der Zusammenfassung in Buchl\u00e4nge"},"content":{"rendered":"

Forscher ver\u00f6ffentlichten eine Studie, in der sie die Genauigkeit und Qualit\u00e4t von Zusammenfassungen verglichen, die LLMs erstellen. Claude 3 Opus schnitt besonders gut ab, aber der Mensch hat immer noch die Nase vorn.<\/strong><\/p>\n

KI-Modelle sind \u00e4u\u00dferst n\u00fctzlich, um lange Dokumente zusammenzufassen, wenn man keine Zeit oder Lust hat, sie zu lesen.<\/p>\n

Der Luxus wachsender Kontextfenster bedeutet, dass wir die Modelle mit l\u00e4ngeren Dokumenten konfrontieren k\u00f6nnen, was ihre F\u00e4higkeit, die Fakten in der Zusammenfassung immer richtig zu erfassen, in Frage stellt.<\/p>\n

Die Forscher von der University of Massachusetts Amherst, Adobe, dem Allen Institute for AI und der Princeton University, ver\u00f6ffentlichte eine Studie<\/a> die herausfinden sollte, wie gut KI-Modelle bei der Zusammenfassung von Inhalten in Buchl\u00e4nge (>100k Token) sind.<\/p>\n

FABLES<\/h2>\n

Sie w\u00e4hlten 26 B\u00fccher aus, die in den Jahren 2023 und 2024 ver\u00f6ffentlicht wurden, und lie\u00dfen verschiedene LLMs die Texte zusammenfassen. Die j\u00fcngsten Ver\u00f6ffentlichungsdaten wurden gew\u00e4hlt, um eine m\u00f6gliche Datenkontamination in den urspr\u00fcnglichen Trainingsdaten der Modelle zu vermeiden.<\/p>\n

Nachdem die Modelle die Zusammenfassungen erstellt hatten, verwendeten sie GPT-4, um daraus dekontextualisierte Aussagen zu extrahieren. Die Forscher stellten dann menschliche Kommentatoren ein, die die B\u00fccher gelesen hatten, und baten sie, die Behauptungen zu \u00fcberpr\u00fcfen.<\/p>\n

\"\"
Das LLM fasst das Buch zusammen, GPT-4 extrahiert die Behauptungen, und menschliche Annotatoren \u00fcberpr\u00fcfen die Behauptungen. Quelle: arXiv<\/figcaption><\/figure>\n

Die daraus resultierenden Daten wurden in einem Datensatz namens \"Faithfulness Annotations for Book-Length Summarization\" (FABLES) zusammengefasst. FABLES enth\u00e4lt 3.158 Annotationen zur Treue in 26 Erz\u00e4hltexten.<\/p>\n

Die Testergebnisse zeigten, dass Claude 3 Opus \"mit deutlichem Abstand die getreueste Zusammenfassung in Buchl\u00e4nge\" war, wobei \u00fcber 90% der Angaben als getreu oder genau verifiziert wurden.<\/p>\n

GPT-4 liegt mit Abstand an zweiter Stelle, denn nur 78% seiner Angaben wurden von den menschlichen Kommentatoren als wahrheitsgetreu best\u00e4tigt.<\/p>\n

\"\"
Prozentsatz der Behauptungen, die aus LLM-generierten Zusammenfassungen extrahiert wurden, die von Menschen als treu, untreu, teilweise Unterst\u00fctzung oder nicht \u00fcberpr\u00fcfbar bewertet wurden. Quelle: arXiv<\/figcaption><\/figure>\n

Der schwierige Teil<\/h2>\n

Die getesteten Modelle schienen alle mit denselben Dingen zu k\u00e4mpfen. Die meisten Fakten, die die Modelle falsch wiedergaben, bezogen sich auf Ereignisse oder Zust\u00e4nde von Personen und Beziehungen.<\/p>\n

Das Papier stellt fest, dass \"die meisten dieser Behauptungen nur durch Multi-Hop-Argumentation \u00fcber die Beweise entkr\u00e4ftet werden k\u00f6nnen, was die Komplexit\u00e4t der Aufgabe und ihren Unterschied zu bestehenden Fakten\u00fcberpr\u00fcfungssituationen hervorhebt\".<\/p>\n

Die LLMs lie\u00dfen in ihren Zusammenfassungen auch h\u00e4ufig kritische Informationen aus. Au\u00dferdem betonen sie den Inhalt gegen Ende des Buches \u00fcberm\u00e4\u00dfig und vernachl\u00e4ssigen wichtige Inhalte am Anfang.<\/p>\n

Wird KI menschliche Kommentatoren ersetzen?<\/h2>\n

Menschliche Kommentatoren oder Faktenchecker sind teuer. Die Forscher gaben $5.200 aus, um die Behauptungen in den KI-Zusammenfassungen durch menschliche Kommentatoren \u00fcberpr\u00fcfen zu lassen.<\/p>\n

H\u00e4tte ein KI-Modell die Aufgabe f\u00fcr weniger Geld erledigen k\u00f6nnen? Einfach Faktenrecherche<\/a> ist etwas, was Claude 3 gut kann, aber seine Leistung bei der \u00dcberpr\u00fcfung von Aussagen, die ein tieferes Verst\u00e4ndnis des Inhalts erfordern, ist weniger konsistent.<\/p>\n

Wenn man ihnen die extrahierten Behauptungen vorlegte und sie aufforderte, diese zu \u00fcberpr\u00fcfen, blieben alle KI-Modelle hinter den menschlichen Kommentatoren zur\u00fcck. Besonders schlecht schnitten sie bei der Erkennung unlauterer Angaben ab.<\/p>\n

Obwohl Claude 3 Opus mit einigem Abstand der beste Antragspr\u00fcfer war, kamen die Forscher zu dem Schluss, dass er \"letztendlich zu schlecht abschneidet, um ein zuverl\u00e4ssiger automatischer Pr\u00fcfer zu sein\".<\/p>\n

Wenn es darum geht, die Nuancen, die komplexen menschlichen Beziehungen, die Handlungspunkte und die Motivationen der Figuren in einer langen Erz\u00e4hlung zu verstehen, scheinen die Menschen im Moment noch im Vorteil zu sein.<\/p>","protected":false},"excerpt":{"rendered":"

Forscher ver\u00f6ffentlichten eine Studie, in der sie die Genauigkeit und Qualit\u00e4t von Zusammenfassungen verglichen, die LLMs erstellen. Claude 3 Opus schnitt besonders gut ab, aber der Mensch ist immer noch im Vorteil. KI-Modelle sind \u00e4u\u00dferst n\u00fctzlich f\u00fcr die Zusammenfassung langer Dokumente, wenn man nicht die Zeit oder Lust hat, sie zu lesen. Der Luxus wachsender Kontextfenster bedeutet, dass wir die Modelle mit l\u00e4ngeren Dokumenten konfrontieren k\u00f6nnen, was ihre F\u00e4higkeit, die Fakten in der Zusammenfassung immer richtig wiederzugeben, in Frage stellt. Die Forscher von der University of Massachusetts Amherst, Adobe, dem Allen Institute for AI und der Princeton University haben eine Studie ver\u00f6ffentlicht, in der sie herausfinden wollten, wie<\/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\/de\/2024\/04\/claude-3-opus-blows-all-llms-away-in-book-length-summarization\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\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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