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arXiv · 2406.10590

LLM-Mediated Domain-Specific Voice Agents: The Case of TextileBot

Shu Zhong·Elia Gatti·James Hardwick·Miriam Ribul·Youngjun Cho·Marianna Obrist
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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.

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Shu Zhong, Elia Gatti, James Hardwick, Miriam Ribul, Youngjun Cho, Marianna Obrist. 2025-02-03. LLM-Mediated Domain-Specific Voice Agents: The Case of TextileBot. https://doi.org/10.1080/0144929x.2025.2456667

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