Walert: Putting Conversational Search Knowledge Into Action By Building And Evaluating A Large Language Model-powered Chatbot

Cherumanal Sachin Pathiyan, Tian Lin, Abushaqra Futoon M., De Paula Angel Felipe Magnossao, Ji Kaixin, Hettiachchi Danula, Trippas Johanne R., Ali Halil, Scholer Falk, Spina Damiano. Arxiv 2024

[Paper] [Code]    
Agentic Applications Has Code Reinforcement Learning Tools

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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