Walert: Putting Conversational Search Knowledge into Action by Building and Evaluating a Large Language Model-Powered Chatbot
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913198536392704 |
|---|---|
| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_07216 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 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 Information Retrieval 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. |
| title | Walert: Putting Conversational Search Knowledge into Action by Building and Evaluating a Large Language Model-Powered Chatbot |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2401.07216 |