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Auflistung nach Autor:in "Yadav, Dipendra"

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  • Konferenzbeitrag
    A Comparative Analysis on Machine Learning Techniques for Research Metadata: the ARDUOUS Case Study
    (INFORMATIK 2024, 2024) Yadav, Dipendra; Tonkin, Emma; Stoev, Teodor; Yordanova, Kristina
    The rapid increase in research publications necessitates effective methods for organizing and analyzing large volumes of textual data. This study evaluates various combinations of embedding models, dimensionality reduction techniques, and clustering algorithms applied to metadata from papers accepted at the ARDUOUS (Annotation of useR Data for UbiquitOUs Systems) workshop over a period of 7 years. The analysis encompasses different types of keywords, including All Keywords (a comprehensive set of all extracted keywords), Multi-word Keywords (phrases consisting of two or more words), Existing Keywords (keywords already present in the metadata), and Single-word Keywords (individual words). The study found that the highest silhouette scores were achieved with 3, 4, and 5 clusters across all keyword types. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were identified as the most effective dimensionality reduction techniques, while DistilBERT embeddings consistently yielded high scores. Clustering algorithms such as k-means, k-medoids, and Gaussian Mixture Models (GMM) demonstrated robustness in forming well-defined clusters. These findings provide valuable insights into the main topics covered in the workshop papers and suggest optimal methodologies for analyzing research metadata, thereby enhancing the understanding of semantic relationships in textual data.
  • Textdokument
    Evaluating Dangerous Capabilities of Large Language Models: An Examination of Situational Awareness
    (DC@KI2023: Proceedings of Doctoral Consortium at KI 2023, 2023) Yadav, Dipendra
    The focal point of this research proposal pertains to a thorough examination of the inherent risks and potential challenges associated with the use of Large Language Models (LLMs). Emphasis has been laid on the facet of situational awareness, an attribute signifying a model’s understanding of its environment, its own state, and the implications of its actions. The proposed research aims to design a robust and reliable metric system and a methodology to gauge situational awareness, followed by an in-depth analysis of major LLMs using this developed metric. The intention is to pinpoint any latent hazards and suggest effective strategies to mitigate these issues, with the ultimate goal of promoting the responsible and secure advancement of artificial intelligence technologies.