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A Neural Corpus Indexer for Document Retrieval

arXiv 2206.02743 ↗First posted 6 Jun 2022cs.IRcited by 107

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Abstract (arXiv)

Current state-of-the-art document retrieval solutions mainly follow an index-retrieve paradigm, where the index is hard to be directly optimized for the final retrieval target. In this paper, we aim to show that an end-to-end deep neural network unifying training and indexing stages can significantly improve the recall performance of traditional methods. To this end, we propose Neural Corpus Indexer (NCI), a sequence-to-sequence network that generates relevant document identifiers directly for a designated query. To optimize the recall performance of NCI, we invent a prefix-aware weight-adaptive decoder architecture, and leverage tailored techniques including query generation, semantic document identifiers, and consistency-based regularization. Empirical studies demonstrated the superiority of NCI on two commonly used academic benchmarks, achieving +21.4% and +16.8% relative enhancement for Recall@1 on NQ320k dataset and R-Precision on TriviaQA dataset, respectively, compared to the best baseline method.

Key facts. Cited by 107 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jun 2022.

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Citing papers per year: 2022: 7; 2023: 23; 2024: 39; 2025: 17; 2026: 21.

107 papers.

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  1. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
  2. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  3. 2023Large Language Models are Effective Text Rankers with Pairwise Ranking Promptingcited by 302
  4. 2023Recommender Systems with Generative Retrievalcited by 191
  5. 2022Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Modelscited by 108
  6. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  7. 2024ToolGen: Unified Tool Retrieval and Calling via Generationcited by 72
  8. 2022Recitation-Augmented Language Modelscited by 65
  9. 2024DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive Learningcited by 52
  10. 2022Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generationcited by 47
  11. 2023How Does Generative Retrieval Scale to Millions of Passages?cited by 47
  12. 2023Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Modelscited by 46
38 more
  1. 2023Learning to Rank in Generative Retrievalcited by 46
  2. 2024Source-Aware Training Enables Knowledge Attribution in Language Modelscited by 29
  3. 2023Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applicationscited by 28
  4. 2024C-RAG: Certified Generation Risks for Retrieval-Augmented Language Modelscited by 27
  5. 2023Large Language Models are Built-in Autoregressive Search Enginescited by 26
  6. 2024Generative Multi-Modal Knowledge Retrieval with Large Language Modelscited by 22
  7. 2023Language Models As Semantic Indexerscited by 20
  8. 2024RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generationcited by 20
  9. 2024Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous Decodingcited by 19
  10. 2023IRGen: Generative Modeling for Image Retrievalcited by 18
  11. 2023GLEN: Generative Retrieval via Lexical Index Learningcited by 17
  12. 2023Multiview Identifiers Enhanced Generative Retrievalcited by 16
  13. 2024Unifying Generative and Dense Retrieval for Sequential Recommendationcited by 16
  14. 2023Constructing Tree-based Index for Efficient and Effective Dense Retrievalcited by 15
  15. 2023UniGen: A Unified Generative Framework for Retrieval and Question Answering with Large Language Modelscited by 14
  16. 2023In-Context Exemplars as Clues to Retrieving from Large Associative Memorycited by 13
  17. 2025SSRL: Self-Search Reinforcement Learningcited by 13
  18. 2023IncDSI: Incrementally Updatable Document Retrievalcited by 12
  19. 2024Dense Passage Retrieval: Is it Retrieving?cited by 12
  20. 2023DORIS-MAE: Scientific Document Retrieval using Multi-level Aspect-based Queriescited by 11
  21. 2023Evaluating Generative Ad Hoc Information Retrievalcited by 11
  22. 2024Generative Dense Retrieval: Memory Can Be a Burdencited by 11
  23. 2024VectorSearch: Enhancing Document Retrieval with Semantic Embeddings and Optimized Searchcited by 10
  24. 2024A Generative Approach for Wikipedia-Scale Visual Entity Recognitioncited by 9
  25. 2024Generative Retrieval with Preference Optimization for E-commerce Searchcited by 9
  26. 2025Lightweight and Direct Document Relevance Optimization for Generative Information Retrievalcited by 9
  27. 2023On the Robustness of Generative Retrieval Models: An Out-of-Distribution Perspectivecited by 8
  28. 2024Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational Searchcited by 8
  29. 2024Beyond Memorization: The Challenge of Random Memory Access in Language Modelscited by 8
  30. 2024Generative Retrieval as Multi-Vector Dense Retrievalcited by 8
  31. 2024Unleash LLMs Potential for Sequential Recommendation by Coordinating Dual Dynamic Index Mechanismcited by 8
  32. 2025A Survey on Retrieval And Structuring Augmented Generation with Large Language Modelscited by 8
  33. 2024Breaking the Hourglass Phenomenon of Residual Quantization: Enhancing the Upper Bound of Generative Retrievalcited by 7
  34. 2025Universal Item Tokenization for Transferable Generative Recommendationcited by 7
  35. 2022Nonparametric Decoding for Generative Retrievalcited by 6
  36. 2022The Infinite Index: Information Retrieval on Generative Text-To-Image Modelscited by 6
  37. 2023Exploring the Practicality of Generative Retrieval on Dynamic Corporacited by 6
  38. 2024Generative Subgraph Retrieval for Knowledge Graph-Grounded Dialog Generationcited by 6

50 most cited of 107 All 107 (JSON)