SemRAG：用于提升问答效果的语义知识增强检索增强生成（RAG）
SemRAG: Semantic Knowledge-Augmented RAG for Improved Question-Answering
AI总结：
本文提出语义知识增强RAG框架SemRAG，通过基于句子嵌入余弦相似度的语义分块和知识图谱整合领域知识，无需大规模微调即可提升问答的检索相关性与准确率，具备高效可扩展的优势。
摘要：
本文提出了SemRAG，这是一种增强型检索增强生成（Retrieval Augmented Generation，RAG）框架，无需大规模微调，即可通过语义分块和知识图谱高效整合领域特定知识。将领域特定知识融入大语言模型（Large Language Models，LLMs）对提升其在专业任务中的表现至关重要，但现有适配方案计算成本高、易过拟合且可扩展性受限。为解决这些挑战，SemRAG采用语义分块算法，基于句子嵌入的余弦相似度对文档进行分段，在保留语义连贯性的同时降低计算开销。此外，SemRAG将检索到的信息构建为知识图谱，捕捉实体间的关系，提升检索准确率和上下文理解能力。在MultiHop RAG和Wikipedia数据集上的实验结果表明，SemRAG显著提升了从知识图谱中检索信息的相关性和正确性，性能优于传统RAG方法。我们还探究了不同语料库的缓冲区大小优化问题，因为针对特定数据集优化缓冲区大小可进一步提升检索性能，而知识图谱的整合强化了实体关系，有助于更好地理解上下文。SemRAG的主要优势在于能够构建高效、准确的领域特定大语言模型流水线，同时避免资源密集型的微调过程。这使其成为一种符合可持续发展目标的实用且可扩展的方案，为领域特定场景下的AI应用提供了可行的解决方案。
英文摘要：
This paper introduces SemRAG, an enhanced Retrieval Augmented Generation (RAG) framework that efficiently integrates domain-specific knowledge using semantic chunking and knowledge graphs without extensive fine-tuning. Integrating domain-specific knowledge into large language models (LLMs) is crucial for improving their performance in specialized tasks. Yet, existing adaptations are computationally expensive, prone to overfitting and limit scalability. To address these challenges, SemRAG employs a semantic chunking algorithm that segments documents based on the cosine similarity from sentence embeddings, preserving semantic coherence while reducing computational overhead. Additionally, by structuring retrieved information into knowledge graphs, SemRAG captures relationships between entities, improving retrieval accuracy and contextual understanding. Experimental results on MultiHop RAG and Wikipedia datasets demonstrate SemRAG has significantly enhances the relevance and correctness of retrieved information from the Knowledge Graph, outperforming traditional RAG methods. Furthermore, we investigate the optimization of buffer sizes for different data corpus, as optimizing buffer sizes tailored to specific datasets can further improve retrieval performance, as integration of knowledge graphs strengthens entity relationships for better contextual comprehension. The primary advantage of SemRAG is its ability to create an efficient, accurate domain-specific LLM pipeline while avoiding resource-intensive fine-tuning. This makes it a practical and scalable approach aligned with sustainability goals, offering a viable solution for AI applications in domain-specific fields.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
