@inproceedings{sanna-etal-2024-building,
title = "Building Certified Medical Chatbots: Overcoming Unstructured Data Limitations with Modular {RAG}",
author = "Sanna, Leonardo and
Bellan, Patrizio and
Magnolini, Simone and
Segala, Marina and
Ghanbari Haez, Saba and
Consolandi, Monica and
Dragoni, Mauro",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Thompson, Paul and
Ondov, Brian",
booktitle = "Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.cl4health-1.15",
pages = "124--130",
abstract = "Creating a certified conversational agent poses several issues. The need to manage fine-grained information delivery and the necessity to provide reliable medical information requires a notable effort, especially in dataset preparation. In this paper, we investigate the challenges of building a certified medical chatbot in Italian that provides information about pregnancy and early childhood. We show some negative initial results regarding the possibility of creating a certified conversational agent within the RASA framework starting from unstructured data. Finally, we propose a modular RAG model to implement a Large Language Model in a certified context, overcoming data limitations and enabling data collection on actual conversations.",
}
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%0 Conference Proceedings
%T Building Certified Medical Chatbots: Overcoming Unstructured Data Limitations with Modular RAG
%A Sanna, Leonardo
%A Bellan, Patrizio
%A Magnolini, Simone
%A Segala, Marina
%A Ghanbari Haez, Saba
%A Consolandi, Monica
%A Dragoni, Mauro
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Thompson, Paul
%Y Ondov, Brian
%S Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F sanna-etal-2024-building
%X Creating a certified conversational agent poses several issues. The need to manage fine-grained information delivery and the necessity to provide reliable medical information requires a notable effort, especially in dataset preparation. In this paper, we investigate the challenges of building a certified medical chatbot in Italian that provides information about pregnancy and early childhood. We show some negative initial results regarding the possibility of creating a certified conversational agent within the RASA framework starting from unstructured data. Finally, we propose a modular RAG model to implement a Large Language Model in a certified context, overcoming data limitations and enabling data collection on actual conversations.
%U https://aclanthology.org/2024.cl4health-1.15
%P 124-130
Markdown (Informal)
[Building Certified Medical Chatbots: Overcoming Unstructured Data Limitations with Modular RAG](https://aclanthology.org/2024.cl4health-1.15) (Sanna et al., CL4Health-WS 2024)
ACL