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Retrieve, Generate, Evaluate: A Case Study for Medical Paraphrases Generation with Small Language Models

arXiv 2407.16565 ↗First posted 23 Jul 2024cs.CLcited by 0

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Abstract (arXiv)

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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31 of 59 references.

References · 31

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19 more
  1. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
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  1. 2024Bailicai: A Domain-Optimized Retrieval-Augmented Generation Framework for Medical Applications
  2. 2024A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences
  3. 2023MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering
  4. 2025Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations
  5. 2025Leveraging Language Models and RAG for Efficient Knowledge Discovery in Clinical Environments
  6. 2024Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report
  7. 2024A Survey for Large Language Models in Biomedicine
  8. 2024Benchmarking Retrieval-Augmented Large Language Models in Biomedical NLP: Application, Robustness, and Self-Awareness