mlsanityData to 14 Oct 2026

Enhanced document retrieval with topic embeddings

arXiv 2408.10435 ↗First posted 19 Aug 2024cs.IRcited by 1

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

Document retrieval systems have experienced a revitalized interest with the advent of retrieval-augmented generation (RAG). RAG architecture offers a lower hallucination rate than LLM-only applications. However, the accuracy of the retrieval mechanism is known to be a bottleneck in the efficiency of these applications. A particular case of subpar retrieval performance is observed in situations where multiple documents from several different but related topics are in the corpus. We have devised a new vectorization method that takes into account the topic information of the document. The paper introduces this new method for text vectorization and evaluates it in the context of RAG. Furthermore, we discuss the challenge of evaluating RAG systems, which pertains to the case at hand.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 10 of its 14 references that are in the map. First posted on arXiv 19 Aug 2024.

Builds on

10 of 14 references.

References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  4. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  5. 2016Enriching Word Vectors with Subword Informationcited by 1,583
  6. 2017Poincar'e Embeddings for Learning Hierarchical Representationscited by 684
  7. 2022mGPT: Few-Shot Learners Go Multilingualcited by 142
  8. 2020Retrieval-Augmented Generation for Code Summarization via Hybrid GNNcited by 89
  9. 2021Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Datacited by 55
  10. 2018Hierarchy-based Image Embeddings for Semantic Image Retrievalcited by 41

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Enhancing Retrieval-Augmented Generation with Topic-Enriched Embeddings: A Hybrid Approach Integrating Traditional NLP Techniquescited by 2

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  1. 2024Observations on Building RAG Systems for Technical Documents
  2. 2024Evaluation of Retrieval-Augmented Generation: A Survey
  3. 2025Are We on the Right Way for Assessing Document Retrieval-Augmented Generation?
  4. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  5. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  6. 2024Evaluating Retrieval Quality in Retrieval-Augmented Generation
  7. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
  8. 2026Progressive Searching for Retrieval in RAG