用主题信息增强嵌入，提升复杂文本的检索精度。
Enhancing Retrieval-Augmented Generation with Topic-Enriched Embeddings: A Hybrid Approach Integrating Traditional NLP Techniques
- 融合词频、主题模型与上下文嵌入，生成多粒度语义表示。
- 在法律文本上使聚类一致性提升18.3%，检索准确率提高12.7%。
- 适合需要高可靠知识检索的场景，如法律、医疗问答系统。
检索增强生成（RAG）系统依赖于精确的文档检索以将大语言模型（LLMs）锚定于外部知识，但在主题重叠和主题变化大的语料中，检索质量常下降。本文提出主题增强嵌入，整合基于词项的信号与主题结构，结合TF-IDF、主题建模与降维技术，利用潜在语义分析（LSA）和潜在狄利克雷分配（LDA）编码隐含主题结构，并将其与紧凑的上下文编码器（all-MiniLM）融合。通过联合捕捉词级与主题级语义，该方法改善了语义聚类，提升了检索精度，同时降低计算开销。在法律文本语料上的实验显示，聚类一致性提升18.3%，检索指标持续增益，表明主题增强嵌入可作为更可靠知识密集型RAG流水线的实际组件。
原文摘要 · Abstract (English)
Retrieval-augmented generation (RAG) systems rely on accurate document retrieval to ground large language models (LLMs) in external knowledge, yet retrieval quality often degrades in corpora where topics overlap and thematic variation is high. This work proposes topic-enriched embeddings that integrate term-based signals and topic structure with contextual sentence embeddings. The approach combines TF-IDF with topic modeling and dimensionality reduction, using Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) to encode latent topical organization, and fuses these representations with a compact contextual encoder (all-MiniLM). By jointly capturing term-level and topic-level semantics, topic-enriched embeddings improve semantic clustering, increase retrieval precision, and reduce computational burden relative to purely contextual baselines. Experiments on a legal-text corpus show consistent gains in clustering coherence and retrieval metrics, suggesting that topic-enriched embeddings can serve as a practical component for more reliable knowledge-intensive RAG pipelines.
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