Enhancing user experience and trust in advanced LLM-based conversational agents

Yuanyuan   Xu; Weiting  Gao; Yining  Wang; Xinyang  Shan; Yin-Shan   Lin

doi:10.59400/cai.v2i2.1467

Enhancing user experience and trust in advanced LLM-based conversational agents

Yuanyuan Xu Tongji University, Shanghai 200092, China
Weiting Gao Amazon, Seattle, WA 98121, USA
Yining Wang Bentley University, Waltham, MA 02452, USA
Xinyang Shan Tongji University, Shanghai 200092, China
Yin-Shan Lin Northeastern University, Boston, MA 02115, USA

Ariticle ID: 1467

257 Views, 99 PDF Downloads

DOI: https://doi.org/10.59400/cai.v2i2.1467

Keywords: large language models (LLMs); user experience (UX); conversational agents; transparency; data security

Abstract

This study explores the enhancement of user experience (UX) and trust in advanced Large Language Model (LLM)-based conversational agents such as ChatGPT. The research involves a controlled experiment comparing participants using an LLM interface with those using a traditional messaging app with a human consultant. The results indicate that LLM-based agents offer higher satisfaction and lower cognitive load, demonstrating the potential for LLMs to revolutionize various applications from customer service to healthcare consultancy and shopping assistance. Despite these positive findings, the study also highlights significant concerns regarding transparency and data security. Participants expressed a need for clearer understanding of how LLMs process information and make decisions. The perceived opacity of these processes can hinder user trust, especially in sensitive applications such as healthcare. Additionally, robust data protection measures are crucial to ensure user privacy and foster trust in these systems. To address these issues, future research and development should focus on enhancing the transparency of LLM operations and strengthening data security protocols. Providing users with clear explanations of how their data is used and how decisions are made can build greater trust. Moreover, specialized applications may require tailored solutions to meet specific user expectations and regulatory requirements. In conclusion, while LLM-based conversational agents have demonstrated substantial advantages in improving user experience, addressing transparency and security concerns is essential for their broader acceptance and effective deployment. By focusing on these areas, developers can create more trustworthy and user-friendly AI systems, paving the way for their integration into diverse fields and everyday use.

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Published

2024-08-17

How to Cite

Xu, Y., Gao, W., Wang, Y., Shan , X., & Lin, Y.-S. (2024). Enhancing user experience and trust in advanced LLM-based conversational agents. Computing and Artificial Intelligence, 2(2), 1467. https://doi.org/10.59400/cai.v2i2.1467

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