A claimed curriculum-based fine-tuning framework for medical LLMs reports better question answering and response generation, but lacks reproducible evidence.
Retrieve, Generate, Evaluate: A Case Study for Medical Paraphrases Generation with Small Language Models
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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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cs.CL 1years
2025 1verdicts
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Generalization of Medical Large Language Models through Cross-Domain Weak Supervision
A claimed curriculum-based fine-tuning framework for medical LLMs reports better question answering and response generation, but lacks reproducible evidence.