Authors:
Erkan Başar
1
;
Iris Hendrickx
2
;
Emiel Krahmer
3
;
Gert-Jan de Bruijn
4
;
5
and
Tibor Bosse
1
Affiliations:
1
Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
;
2
Centre for Language Studies, Radboud University, Nijmegen, The Netherlands
;
3
Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, The Netherlands
;
4
Department of Communication Studies, University of Antwerp, Antwerp, Belgium
;
5
Faculty of Social and Behavioural Sciences, University of Amsterdam, Amsterdam, The Netherlands
Keyword(s):
Hybrid Conversational Agents, Task-oriented Dialogue Systems, Multi-turn Response Selection, Natural Language Generation.
Abstract:
Open-domain large language models have progressed to generating natural-sounding and coherent text. Even though the generated texts appear human-like, the main stumbling block is that their output is never fully predictable, which runs the risk of resulting in harmful content such as false statements or inflammatory language. This makes it difficult to apply these models in highly sensitive domains including personal health counselling. Hence, most of the chatbots for highly sensitive domains are developed using pre-scripted approaches. Although pre-scripted approaches are highly controlled, they suffer from repetitiveness and scalability issues. In this paper, we explore the possibility of combining the best of both worlds. We propose and describe in detail a new, flexible expert-driven hybrid architecture for harnessing the benefits of large language models in a controlled manner for highly sensitive domains and discuss the expectations and challenges.