@inproceedings{buhnila-etal-2024-retrieve,
title = "Retrieve, Generate, Evaluate: A Case Study for Medical Paraphrases Generation with Small Language Models",
author = "Buhnila, Ioana and
Sinha, Aman and
Constant, Mathieu",
editor = "Li, Sha and
Li, Manling and
Zhang, Michael JQ and
Choi, Eunsol and
Geva, Mor and
Hase, Peter and
Ji, Heng",
booktitle = "Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowllm-1.16",
pages = "189--203",
abstract = "Recent surge in the accessibility of large language models (LLMs) to the general population can lead to untrackable use of such models for medical-related recommendations. Language generation via LLMs models has two key problems: firstly, they are prone to hallucination and therefore, for any medical purpose they require scientific and factual grounding; secondly, LLMs pose tremendous challenge to computational resources due to their gigantic model size. In this work, we introduce pRAGe, a Pipeline for Retrieval Augmented Generation and Evaluation of medical paraphrases generation using Small Language Models (SLM). We study the effectiveness of SLMs and the impact of external knowledge base for medical paraphrase generation in French.",
}
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%0 Conference Proceedings
%T Retrieve, Generate, Evaluate: A Case Study for Medical Paraphrases Generation with Small Language Models
%A Buhnila, Ioana
%A Sinha, Aman
%A Constant, Mathieu
%Y Li, Sha
%Y Li, Manling
%Y Zhang, Michael JQ
%Y Choi, Eunsol
%Y Geva, Mor
%Y Hase, Peter
%Y Ji, Heng
%S Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F buhnila-etal-2024-retrieve
%X Recent surge in the accessibility of large language models (LLMs) to the general population can lead to untrackable use of such models for medical-related recommendations. Language generation via LLMs models has two key problems: firstly, they are prone to hallucination and therefore, for any medical purpose they require scientific and factual grounding; secondly, LLMs pose tremendous challenge to computational resources due to their gigantic model size. In this work, we introduce pRAGe, a Pipeline for Retrieval Augmented Generation and Evaluation of medical paraphrases generation using Small Language Models (SLM). We study the effectiveness of SLMs and the impact of external knowledge base for medical paraphrase generation in French.
%U https://aclanthology.org/2024.knowllm-1.16
%P 189-203
Markdown (Informal)
[Retrieve, Generate, Evaluate: A Case Study for Medical Paraphrases Generation with Small Language Models](https://aclanthology.org/2024.knowllm-1.16) (Buhnila et al., KnowLLM-WS 2024)
ACL