Walert: Putting Conversational Search Knowledge into Action by Building and Evaluating a Large Language Model-Powered Chatbot
CoRR(2024)
摘要
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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