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arXiv · 2401.07216

Walert: Putting Conversational Search Knowledge into Action by Building and Evaluating a Large Language Model-Powered Chatbot

Sachin Pathiyan Cherumanal·Lin Tian·Futoon M. Abushaqra·Angel Felipe Magnossao de Paula·Kaixin Ji·Danula Hettiachchi·Johanne R. Trippas·Halil Ali·Falk Scholer·Damiano Spina
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Abstract

Creating and deploying customized applications is crucial for operational success and enriching user experiences in the rapidly evolving modern business world. A prominent facet of modern user experiences is the integration of chatbots or voice assistants. The rapid evolution of Large Language Models (LLMs) has provided a powerful tool to build conversational applications. We present Walert, a customized LLM-based conversational agent able to answer frequently asked questions about computer science degrees and programs at RMIT University. Our demo aims to showcase how conversational information-seeking researchers can effectively communicate the benefits of using best practices to stakeholders interested in developing and deploying LLM-based chatbots. These practices are well-known in our community but often overlooked by practitioners who may not have access to this knowledge. The methodology and resources used in this demo serve as a bridge to facilitate knowledge transfer from experts, address industry professionals' practical needs, and foster a collaborative environment. The data and code of the demo are available at https://github.com/rmit-ir/walert.

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Sachin Pathiyan Cherumanal, Lin Tian, Futoon M. Abushaqra, Angel Felipe Magnossao de Paula, Kaixin Ji, Danula Hettiachchi, Johanne R. Trippas, Halil Ali, Falk Scholer, Damiano Spina. 2024-01-14. Walert: Putting Conversational Search Knowledge into Action by Building and Evaluating a Large Language Model-Powered Chatbot. https://doi.org/10.1145/3627508.3638309

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