CITATION.bib

@inproceedings{pathiyan2024walert,
author = {Pathiyan Cherumanal, Sachin and Tian, Lin and Abushaqra, Futoon M. and Magnoss\~{a}o de Paula, Angel Felipe and Ji, Kaixin and Ali, Halil and Hettiachchi, Danula and Trippas, Johanne R. and Scholer, Falk and Spina, Damiano},
title = {Walert: Putting Conversational Information Seeking Knowledge into Action by Building and Evaluating a Large Language Model-Powered Chatbot},
year = {2024},
isbn = {9798400704345},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3627508.3638309},
doi = {10.1145/3627508.3638309},
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.},
booktitle = {Proceedings of the 2024 Conference on Human Information Interaction and Retrieval},
pages = {401–405},
numpages = {5},
keywords = {conversational information seeking, large language models, retrieval-augmented generation},
location = {<conf-loc>, <city>Sheffield</city>, <country>United Kingdom</country>, </conf-loc>},
series = {CHIIR '24}
}