Search Results for author: Sebastian Schmidt

Found 6 papers, 2 papers with code

Developing trustworthy AI applications with foundation models

no code implementations8 May 2024 Michael Mock, Sebastian Schmidt, Felix Müller, Rebekka Görge, Anna Schmitz, Elena Haedecke, Angelika Voss, Dirk Hecker, Maximillian Poretschkin

Chapter 2 provides an introduction to the technical construction of foundation models and Chapter 3 shows how AI applications can be developed based on them.

Detecting Generated Native Ads in Conversational Search

2 code implementations7 Feb 2024 Sebastian Schmidt, Ines Zelch, Janek Bevendorff, Benno Stein, Matthias Hagen, Martin Potthast

In this paper, we thus take a first step to investigate whether LLMs can also be used as a countermeasure, i. e., to block generated native ads.

Conversational Search Sentence

Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss

no code implementations11 Sep 2023 Sebastian Schmidt, Stephan Günnemann

We exploited the temporal properties for such image streams in our work and proposed the novel temporal predicted loss (TPL) method.

Active Learning

FH-SWF SG at GermEval 2021: Using Transformer-Based Language Models to Identify Toxic, Engaging, & Fact-Claiming Comments

no code implementations GermEval 2021 Christian Gawron, Sebastian Schmidt

In this paper we describe the methods we used for our submissions to the GermEval 2021 shared task on the identification of toxic, engaging, and fact-claiming comments.

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