no code implementations • 12 Mar 2024 • Thilo Spinner, Rebecca Kehlbeck, Rita Sevastjanova, Tobias Stähle, Daniel A. Keim, Oliver Deussen, Mennatallah El-Assady
Large language models (LLMs) are widely deployed in various downstream tasks, e. g., auto-completion, aided writing, or chat-based text generation.
no code implementations • 17 Oct 2023 • Thilo Spinner, Rebecca Kehlbeck, Rita Sevastjanova, Tobias Stähle, Daniel A. Keim, Oliver Deussen, Andreas Spitz, Mennatallah El-Assady
We quantitatively show the value of exposing the beam search tree and present five detailed analysis scenarios addressing the identified challenges.
1 code implementation • 29 Jul 2019 • Thilo Spinner, Udo Schlegel, Hanna Schäfer, Mennatallah El-Assady
We propose a framework for interactive and explainable machine learning that enables users to (1) understand machine learning models; (2) diagnose model limitations using different explainable AI methods; as well as (3) refine and optimize the models.
BIG-bench Machine Learning Explainable Artificial Intelligence (XAI)
1 code implementation • 3 May 2019 • Jochen Görtler, Thilo Spinner, Dirk Streeb, Daniel Weiskopf, Oliver Deussen
We present a technique to perform dimensionality reduction on data that is subject to uncertainty.