mlsanityData to 7 Oct 2026

SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking

arXiv 2107.05720 ↗First posted 12 Jul 2021cs.IRcited by 283

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Cited by 48 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In neural Information Retrieval, ongoing research is directed towards improving the first retriever in ranking pipelines. Learning dense embeddings to conduct retrieval using efficient approximate nearest neighbors methods has proven to work well. Meanwhile, there has been a growing interest in learning sparse representations for documents and queries, that could inherit from the desirable properties of bag-of-words models such as the exact matching of terms and the efficiency of inverted indexes. In this work, we present a new first-stage ranker based on explicit sparsity regularization and a log-saturation effect on term weights, leading to highly sparse representations and competitive results with respect to state-of-the-art dense and sparse methods. Our approach is simple, trained end-to-end in a single stage. We also explore the trade-off between effectiveness and efficiency, by controlling the contribution of the sparsity regularization.

Key facts. Cited by 283 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jul 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 21; 2023: 28; 2024: 51; 2025: 76; 2026: 102.

283 papers.

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  1. 2021ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interactioncited by 464
  2. 2023RA-DIT: Retrieval-Augmented Dual Instruction Tuningcited by 194
  3. 2023Query2doc: Query Expansion with Large Language Modelscited by 146
  4. 2021SPLADE v2: Sparse Lexical and Expansion Model for Information Retrievalcited by 126
  5. 2022Autoregressive Search Engines: Generating Substrings as Document Identifierscited by 112
  6. 2022From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effectivecited by 98
  7. 2022PLAID: An Efficient Engine for Late Interaction Retrievalcited by 95
  8. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  9. 2024SPLADE-v3: New baselines for SPLADEcited by 69
  10. 2024When Search Engine Services meet Large Language Models: Visions and Challengescited by 59
  11. 2022Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generationcited by 47
  12. 2023Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Rankingcited by 44
38 more
  1. 2025REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmarkcited by 34
  2. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generationcited by 34
  3. 2023WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpuscited by 29
  4. 2022What Are You Token About? Dense Retrieval as Distributions Over the Vocabularycited by 28
  5. 2024Scaling Laws For Dense Retrievalcited by 28
  6. 2022An Efficiency Study for SPLADE Modelscited by 26
  7. 2024Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retrievercited by 25
  8. 2021A Proposed Conceptual Framework for a Representational Approach to Information Retrievalcited by 24
  9. 2023STAIR: Learning Sparse Text and Image Representation in Grounded Tokenscited by 23
  10. 2024Reducing the Footprint of Multi-Vector Retrieval with Minimal Performance Impact via Token Poolingcited by 23
  11. 2024Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and Distillationcited by 22
  12. 2024A Survey of Conversational Searchcited by 22
  13. 2022In Defense of Cross-Encoders for Zero-Shot Retrievalcited by 21
  14. 2024Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and Challengescited by 20
  15. 2022UnifieR: A Unified Retriever for Large-Scale Retrievalcited by 19
  16. 2022CITADEL: Conditional Token Interaction via Dynamic Lexical Routing for Efficient and Effective Multi-Vector Retrievalcited by 19
  17. 2024Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous Decodingcited by 19
  18. 2023GLEN: Generative Retrieval via Lexical Index Learningcited by 17
  19. 2024Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentationcited by 16
  20. 2022LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrievalcited by 15
  21. 2025Lost at the Beginning of Reasoningcited by 15
  22. 2024PIRB: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methodscited by 14
  23. 2024Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucinationcited by 14
  24. 2024AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignmentcited by 14
  25. 2022Multi-Vector Retrieval as Sparse Alignmentcited by 12
  26. 2024Multimodal Learned Sparse Retrieval with Probabilistic Expansion Controlcited by 12
  27. 2025Interpretable Text Embeddings and Text Similarity Explanation: A Surveycited by 12
  28. 2023BERM: Training the Balanced and Extractable Representation for Matching to Improve Generalization Ability of Dense Retrievalcited by 11
  29. 2023Augmenting Black-box LLMs with Medical Textbooks for Biomedical Question Answeringcited by 11
  30. 2023Evaluating Generative Ad Hoc Information Retrievalcited by 11
  31. 2024Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomycited by 11
  32. 2024Retrieval-Enhanced Machine Learning: Synthesis and Opportunitiescited by 11
  33. 2025ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract Draftingcited by 11
  34. 2021Match Your Words! A Study of Lexical Matching in Neural Information Retrievalcited by 10
  35. 2023CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answeringcited by 10
  36. 2026GrepSeek: Training Search Agents for Direct Corpus Interactioncited by 10
  37. 2022Query-as-context Pre-training for Dense Passage Retrievalcited by 9
  38. 2025ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrievalcited by 9

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