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How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models

Titelangaben

Dang, Hai ; Mecke, Lukas ; Lehmann, Florian ; Goller, Sven ; Buschek, Daniel:
How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models.
2022
Veranstaltung: ACM CHI Conference on Human Factors in Computing Systems - Workshops , 10.05.2022 , Online.
(Veranstaltungsbeitrag: Workshop , Vortrag mit Paper )

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Angaben zu Projekten

Projekttitel:
Offizieller Projekttitel
Projekt-ID
AI Tools - Continuous Interaction with Computational Intelligence Tools
Ohne Angabe

Abstract

Deep generative models have the potential to fundamentally change the way we create high-fidelity digital content but are often hard to control. Prompting a generative model is a promising recent development that in principle enables end-users to creatively leverage zero-shot and few-shot learning to assign new tasks to an AI ad-hoc, simply by writing them down. However, for the majority of end-users writing effective prompts is currently largely a trial and error process. To address this, we discuss the key opportunities and challenges for interactive creative applications that use prompting as a new paradigm for Human-AI interaction. Based on our analysis, we propose four design goals for user interfaces that support prompting. We illustrate these with concrete UI design sketches, focusing on the use case of creative writing. The research community in HCI and AI can take these as starting points to develop adequate user interfaces for models capable of zero- and few-shot learning.

Weitere Angaben

Publikationsform: Veranstaltungsbeitrag (Vortrag mit Paper)
Begutachteter Beitrag: Ja
Institutionen der Universität: Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik
Fakultäten
Fakultäten > Fakultät für Mathematik, Physik und Informatik
Titel an der UBT entstanden: Ja
Themengebiete aus DDC: 000 Informatik,Informationswissenschaft, allgemeine Werke > 004 Informatik
Eingestellt am: 09 Aug 2022 06:53
Letzte Änderung: 09 Aug 2022 06:53
URI: https://eref.uni-bayreuth.de/id/eprint/71428