用检索增强生成技术让大模型更精准地推荐适配阅读难度的文章
Combining Retrieval-Augmented Text Generation with LLMs for Reading Content Recommendations
- 结合检索与大模型生成，根据用户提问和难度要求定制文章
- 引入RAG后内容相关性提升26-35个百分点，更真实可信
- 适合需要个性化内容生成的教育、出版和信息推荐场景
本研究设计并评估了一个基于大语言模型（LLMs）与检索增强生成（RAG）结合的个性化阅读内容生成系统。该架构包含输入、RAG、生成和评判四个模块，支持用户指定问题及目标阅读复杂度。RAG从互联网检索相关信息，增强并校准三个现代LLM（Meta LLaMA 4 Scout、LLaMA 3.1 8B Instant、Google Gemma2 9B）的输出。采用链式思维、零样本和少样本三种提示策略生成文本，由LLM-as-a-Judge模块自动评估答案质量与预期可读性的一致性。实验表明，所有模型和提示方法下，RAG均显著提升性能，相关性和可信赖度最高提升26–35个百分点。结果证明，RAG增强架构能有效生成符合用户查询与文本复杂度需求的阅读材料。
原文摘要 · Abstract (English)
This work presents the design, implementation, and evaluation of a system for generating personalized reading content using Large Language Models (LLMs) combined with Retrieval-Augmented Generation (RAG). The proposed architecture consists of four modules: Input, RAG, Generation, and Judging and enables users to specify both a question and a target reading content complexity. RAG is employed to retrieve relevant information from the Internet, enriching and grounding the content produced by three modern LLMs: Meta LLaMA 4 Scout, LLaMA 3.1 8B Instant, and Google Gemma2 9B. Reading materials are generated using three prompting strategies (Chain-of-Thought, zero-shot, and few-shot), and the LLM-as-a-Judge module automatically evaluates answer quality and alignment with the desired readability level. Experimental results show that RAG consistently improves system performance across all models and prompting techniques, increasing relevance and particularly groundedness by up to 26-35 percentage points. Overall, the findings demonstrate that the RAG-augmented architecture effectively produces reading content tailored to user queries and desired textual complexity.
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