Zhong, Shu;
Gatti, Elia;
Hardwick, James;
Ribul, Miriam;
Cho, Youngjun;
Obrist, Marianna;
(2025)
LLM-mediated domain-specific voice agents: the case of TextileBot.
Behaviour and Information Technology
10.1080/0144929X.2025.2456667.
(In press).
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Abstract
Developing domain-specific conversational agents (CAs) has been challenged by the need for extensive domain-focused data. Recent advancements in Large Language Models (LLMs) make them a viable option as a knowledge backbone. LLMs behaviour can be enhanced through prompting, instructing them to perform downstream tasks in a zero-shot fashion (i.e. without training). To this end, we incorporated structural knowledge into prompts and used prompted LLMs to prototyping domain-specific CAs. We demonstrate a case study in a specific domain-textile circularity – TextileBot, we present the design, development, and evaluation of the TextileBot. Specially, we conducted an in-person user study (N = 30) with Free Chat and Information-Gathering tasks with TextileBots to gather insights from the interaction. We analyse the human–agent interactions, combining quantitative and qualitative methods. Our results suggest that participants engaged in multi-turn conversations, and their perceptions of the three variation agents and respective interactions varied demonstrating the effectiveness of our prompt-based LLM approach. We discuss the dynamics of these interactions and their implications for designing future voice-based CAs.
Type: | Article |
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Title: | LLM-mediated domain-specific voice agents: the case of TextileBot |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1080/0144929X.2025.2456667 |
Publisher version: | https://doi.org/10.1080/0144929X.2025.2456667 |
Language: | English |
Additional information: | © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. |
Keywords: | Human–AI interactions; domain-specific conversational agents; large language models; prompt engineering; voice agents; sustainability |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10204301 |
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