# Enhancing Retrieval-Augmented Generation with Topic-Enriched Embeddings: A Hybrid Approach Integrating Traditional NLP Techniques

arXiv: https://arxiv.org/abs/2601.00891 · HTML: https://mlsanity.com/paper/2601.00891/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation.
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 31 Dec 2025.

## Placed in

- Retrieval-augmented generation / [Generation and context integration](https://mlsanity.com/rag/generation-and-context-integration/)

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Precise Zero-Shot Dense Retrieval without Relevance Labels](https://mlsanity.com/paper/2212.10496/) (2022)
- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [The Chronicles of RAG: The Retriever, the Chunk and the Generator](https://mlsanity.com/paper/2401.07883/) (2024)
- [Hierarchical Interpretation of Neural Text Classification](https://mlsanity.com/paper/2202.09792/) (2022)
- [Enhanced document retrieval with topic embeddings](https://mlsanity.com/paper/2408.10435/) (2024)

## Built on by (most cited first, 2 of 2)

- [An Agentic AI Framework with Large Language Models and Chain-of-Thought for UAV-Assisted Logistics Scheduling with Mobile Edge Computing](https://mlsanity.com/paper/2605.13221/) (2026)
- [ViSAR: Training-Free Adaptive-k Retrieval for Visual Document Question Answering](https://mlsanity.com/paper/2609.02486/) (2026)

## Abstract (arXiv)

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.