This paper introduces the RAG-RLRC-LaySum framework, designed to make complex
biomedical research understandable to laymen through advanced Natural Language
Processing (NLP) techniques. Our Retrieval Augmented Generation (RAG) solution,
enhanced by a reranking method, utilizes multiple knowledge sources to ensure
the precision and pertinence of lay summaries. Additionally, our Reinforcement
Learning for Readability Control (RLRC) strategy improves readability, making
scientific content comprehensible to non-specialists. Evaluations using the
publicly accessible PLOS and eLife datasets show that our methods surpass Plain
Gemini model, demonstrating a 20% increase in readability scores, a 15%
improvement in ROUGE-2 relevance scores, and a 10% enhancement in factual
accuracy. The RAG-RLRC-LaySum framework effectively democratizes scientific
knowledge, enhancing public engagement with biomedical discoveries.

该论文介绍了 RAG-RLRC-LaySum 框架，通过先进的自然语言处理技术，使复杂的生物医学研究能够被非专业人士理解。我们的 RAG 解决方案借助重新排序方法利用多个知识源来确保准确性和相关性，同时我们的 RLRC 策略通过提高可读性使科学内容易于理解。在使用公开可访问的 PLOS 和 eLife 数据集进行评估时，我们的方法超过了 Plain Gemini 模型，读者易读性得分提高了 20%，ROUGE-2 相关性得分提高了 15%，事实准确性提高了 10%。RAG-RLRC-LaySum 框架能够有效地推广科学知识，促进公众对生物医学发现的参与。