Suche mittels Attribut

Diese Seite stellt eine einfache Suchoberfläche zum Finden von Objekten bereit, die ein Attribut mit einem bestimmten Datenwert enthalten. Andere verfügbare Suchoberflächen sind die Attributsuche sowie der Abfragengenerator.

Suche mittels Attribut

Eine Liste aller Seiten, die das Attribut „Kurzfassung“ mit dem Wert „The automatic identification of requirements, and their classification according to their security objectives, can be helpful to derive insights into the security of a given system. However, this task requires significant security expertise to perform. In this thesis, the capability of modern Large Language Models (such as GPT) to replicate this expertise is investigated. This requires the transfer of the model's understanding of language to the given specific task. In particular, different prompt engineering approaches are combined and compared, in order to gain insights into their effects on performance. GPT ultimately performs poorly for the main tasks of identification of requirements and of their classification according to security objectives. Conversely, the model performs well for the sub-task of classifying the security-relevance of requirements. Interestingly, prompt components influencing the format of the model's output seem to have a higher performance impact than components containing contextual information.“ haben. Weil nur wenige Ergebnisse gefunden wurden, werden auch ähnliche Werte aufgelistet.

Hier sind 2 Ergebnisse, beginnend mit Nummer 1.

Zeige (vorherige 50 | nächste 50) (20 | 50 | 100 | 250 | 500)

    

Liste der Ergebnisse

    • Identifying Security Requirements in Natural Language Documents  + (The automatic identification of requiremen … The automatic identification of requirements, and their classification according to their security objectives, can be helpful to derive insights into the security of a given system. However, this task requires significant security expertise to perform. In this thesis, the capability of modern Large Language Models (such as GPT) to replicate this expertise is investigated. This requires the transfer of the model's understanding of language to the given specific task. In particular, different prompt engineering approaches are combined and compared, in order to gain insights into their effects on performance. GPT ultimately performs poorly for the main tasks of identification of requirements and of their classification according to security objectives. Conversely, the model performs well for the sub-task of classifying the security-relevance of requirements. Interestingly, prompt components influencing the format of the model's output seem to have a higher performance impact than components containing contextual information.ponents containing contextual information.)
    Abgerufen von „https://sdq.kastel.kit.edu/institutsseminar/Spezial:Suche_mittels_Attribut“