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Found 2 papers, 1 papers with code
RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts
This paper introduces the RAG-RLRC-LaySum framework, designed to make complex biomedical research understandable to laymen through advanced Natural Language Processing (NLP) techniques.
Effects of Different Prompts on the Quality of GPT-4 Responses to Dementia Care Questions
Evidence suggests that different prompts lead large language models (LLMs) to generate responses with varying quality.
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