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    <abstract>Large Language Models show impressive results in many different applications, most notably in the context of question-answering and open dialog situations. However, it is still an open question how to use those models for task-oriented dialogs such as booking or customer information systems, and such. In this work, we propose Dynamic Prompting, an architecture for task-oriented dialog, integrating the benefits of Large Language Models and showcasing the approach on the MultiWOZ 2.2 dataset. Our architecture leads to a high task success rate, provides sensible and specific answers, and is resistant to hallucinations. Further, we show that Dynamic Prompting is able to answer questions that were not anticipated by the dialog systems designer and that it can correct several types of errors and other characteristics of the system.</abstract>
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%0 Conference Proceedings
%T Dynamic Prompting: Large Language Models for Task Oriented Dialog
%A Nehring, Jan
%A Juneja, Akhil
%A Ahmad, Adnan
%A Roller, Roland
%A Klakow, Dietrich
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F nehring-etal-2024-dynamic
%X Large Language Models show impressive results in many different applications, most notably in the context of question-answering and open dialog situations. However, it is still an open question how to use those models for task-oriented dialogs such as booking or customer information systems, and such. In this work, we propose Dynamic Prompting, an architecture for task-oriented dialog, integrating the benefits of Large Language Models and showcasing the approach on the MultiWOZ 2.2 dataset. Our architecture leads to a high task success rate, provides sensible and specific answers, and is resistant to hallucinations. Further, we show that Dynamic Prompting is able to answer questions that were not anticipated by the dialog systems designer and that it can correct several types of errors and other characteristics of the system.
%U https://aclanthology.org/2024.clicit-1.72/
%P 644-653

Markdown (Informal)

[Dynamic Prompting: Large Language Models for Task Oriented Dialog](https://aclanthology.org/2024.clicit-1.72/) (Nehring et al., CLiC-it 2024)

• Dynamic Prompting: Large Language Models for Task Oriented Dialog (Nehring et al., CLiC-it 2024)

ACL

• Jan Nehring, Akhil Juneja, Adnan Ahmad, Roland Roller, and Dietrich Klakow. 2024. Dynamic Prompting: Large Language Models for Task Oriented Dialog. In Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024), pages 644–653, Pisa, Italy. CEUR Workshop Proceedings.