mlsanityData to 7 Oct 2026

LLM-Augmented Retrieval: Enhancing Retrieval Models Through Language Models and Doc-Level Embedding

arXiv 2404.05825 ↗First posted 8 Apr 2024cs.IR cs.AIcited by 6

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Abstract (arXiv)

Recently embedding-based retrieval or dense retrieval have shown state of the art results, compared with traditional sparse or bag-of-words based approaches. This paper introduces a model-agnostic doc-level embedding framework through large language model (LLM) augmentation. In addition, it also improves some important components in the retrieval model training process, such as negative sampling, loss function, etc. By implementing this LLM-augmented retrieval framework, we have been able to significantly improve the effectiveness of widely-used retriever models such as Bi-encoders (Contriever, DRAGON) and late-interaction models (ColBERTv2), thereby achieving state-of-the-art results on LoTTE datasets and BEIR datasets.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 24 of its 41 references that are in the map. First posted on arXiv 8 Apr 2024.

Builds on

24 of 41 references.

References · 24

  1. 2017Attention Is All You Needcited by 59,932
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  6. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  7. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  8. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,225
  9. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
  10. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,160
  11. 2019Passage Re-ranking with BERTcited by 629
  12. 2021ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interactioncited by 464
12 more
  1. 2023Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agentscited by 356
  2. 2021SPLADE: Sparse Lexical and Expansion Model for First Stage Rankingcited by 283
  3. 2022Generating Training Data with Language Models: Towards Zero-Shot Language Understandingcited by 199
  4. 2021Generating Datasets with Pretrained Language Modelscited by 192
  5. 2019Not Enough Data? Deep Learning to the Rescue!cited by 174
  6. 2023How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrievalcited by 116
  7. 2022From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effectivecited by 98
  8. 2020DARE: Data Augmented Relation Extraction with GPT-2cited by 43
  9. 2019An Alternative Cross Entropy Loss for Learning-to-Rankcited by 17
  10. 2021Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrievalcited by 14
  11. 2019Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective Functioncited by 3
  12. 2023Offline Pseudo Relevance Feedback for Efficient and Effective Single-pass Dense Retrievalcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 1.

6 papers.

  1. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrievalcited by 31
  2. 2025OpenRAG: Optimizing RAG End-to-End via In-Context Retrieval Learningcited by 4
  3. 2025Context-Efficient Retrieval with Factual Decompositioncited by 2
  4. 2024REFINE on Scarce Data: Retrieval Enhancement through Fine-Tuning via Model Fusion of Embedding Modelscited by 1
  5. 2025LMAR: Language Model Augmented Retriever for Domain-specific Knowledge Indexingcited by 1
  6. 2026SSR-GRPO: Integrating Supervision and Semantic IDs into Reinforcement Learning for Dense Retrieval in E-commercecited by 0