mlsanityData to 7 Oct 2026

Recitation-Augmented Language Models

arXiv 2210.01296 ↗First posted 4 Oct 2022cs.CL cs.AIcited by 65

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Abstract (arXiv)

We propose a new paradigm to help Large Language Models (LLMs) generate more accurate factual knowledge without retrieving from an external corpus, called RECITation-augmented gEneration (RECITE). Different from retrieval-augmented language models that retrieve relevant documents before generating the outputs, given an input, RECITE first recites one or several relevant passages from LLMs' own memory via sampling, and then produces the final answers. We show that RECITE is a powerful paradigm for knowledge-intensive NLP tasks. Specifically, we show that by utilizing recitation as the intermediate step, a recite-and-answer scheme can achieve new state-of-the-art performance in various closed-book question answering (CBQA) tasks. In experiments, we verify the effectiveness of method~on four pre-trained models (PaLM, UL2, OPT, and Codex) and three CBQA tasks (Natural Questions, TriviaQA, and HotpotQA). Our code is available at "https://github.com/Edward-Sun/RECITE".

Key facts. Cited by 65 papers in the map (data through 7 Oct 2026). Builds on 56 of its 73 references that are in the map. First posted on arXiv 4 Oct 2022.

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56 of 73 references.

References · 56

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  2. 2020Language Models are Few-Shot Learnerscited by 32,199
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  6. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
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  8. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  9. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  10. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  11. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  12. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
38 more
  1. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  2. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  3. 2022Training Compute-Optimal Large Language Modelscited by 2,927
  4. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
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  10. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
  11. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
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  14. 2019How Can We Know What Language Models Know?cited by 953
  15. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  16. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 847
  17. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
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  19. 2020How Much Knowledge Can You Pack Into the Parameters of a Language Model?cited by 713
  20. 2019MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalismscited by 653
  21. 2017Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problemscited by 642
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  24. 2022Transformer Memory as a Differentiable Search Indexcited by 242
  25. 2020Distilling Knowledge from Reader to Retriever for Question Answeringcited by 226
  26. 2022Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Modelscited by 218
  27. 2021Generated Knowledge Prompting for Commonsense Reasoningcited by 213
  28. 2019oLMpics -- On what Language Model Pre-training Capturescited by 161
  29. 2020Unsupervised Commonsense Question Answering with Self-Talkcited by 161
  30. 2021Counterfactual Memorization in Neural Language Modelscited by 155
  31. 2019End-to-End Open-Domain Question Answering with BERTserinicited by 152
  32. 2020WT5?! Training Text-to-Text Models to Explain their Predictionscited by 148
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  34. 2022Internet-augmented language models through few-shot prompting for open-domain question answeringcited by 131
  35. 2022Autoregressive Search Engines: Generating Substrings as Document Identifierscited by 112
  36. 2022Rationale-Augmented Ensembles in Language Modelscited by 111
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  38. 2021Can Generative Pre-trained Language Models Serve as Knowledge Bases for Closed-book QA?cited by 62

50 most cited of 56 All 56 (JSON)

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Citing papers per year: 2022: 4; 2023: 30; 2024: 15; 2025: 9; 2026: 7.

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  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  2. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  3. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  4. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 803
  5. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 519
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  7. 2023Towards Expert-Level Medical Question Answering with Large Language Modelscited by 382
  8. 2023Language Models can Solve Computer Taskscited by 371
  9. 2023Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflictscited by 333
  10. 2023Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervisioncited by 318
  11. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 316
  12. 2023FreshLLMs: Refreshing Large Language Models with Search Engine Augmentationcited by 308
38 more
  1. 2023Tool Learning with Foundation Modelscited by 302
  2. 2023Can Language Models Solve Graph Problems in Natural Language?cited by 249
  3. 2024The Effect of Sampling Temperature on Problem Solving in Large Language Modelscited by 168
  4. 2023Take a Step Back: Evoking Reasoning via Abstraction in Large Language Modelscited by 157
  5. 2023Query2doc: Query Expansion with Large Language Modelscited by 146
  6. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generationcited by 144
  7. 2023Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!cited by 119
  8. 2025Memento: Fine-tuning LLM Agents without Fine-tuning LLMscited by 119
  9. 2023Large Language Models as Analogical Reasonerscited by 107
  10. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  11. 2023Answering Questions by Meta-Reasoning over Multiple Chains of Thoughtcited by 87
  12. 2023Improving Language Models via Plug-and-Play Retrieval Feedbackcited by 87
  13. 2023Towards Mitigating Hallucination in Large Language Models via Self-Reflectioncited by 81
  14. 2024Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalizationcited by 78
  15. 2025Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learningcited by 68
  16. 2023Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Modelscited by 46
  17. 2023Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistencycited by 46
  18. 2025Seek in the Dark: Reasoning via Test-Time Instance-Level Policy Gradient in Latent Spacecited by 40
  19. 2022Self-Prompting Large Language Models for Zero-Shot Open-Domain QAcited by 39
  20. 2023Joint Prompt Optimization of Stacked LLMs using Variational Inferencecited by 32
  21. 2023Retrieval-Generation Synergy Augmented Large Language Modelscited by 26
  22. 2023QACHECK: A Demonstration System for Question-Guided Multi-Hop Fact-Checkingcited by 26
  23. 2024SiReRAG: Indexing Similar and Related Information for Multihop Reasoningcited by 23
  24. 2023An Empirical Study of Translation Hypothesis Ensembling with Large Language Modelscited by 16
  25. 2023On the Trustworthiness Landscape of State-of-the-art Generative Models: A Survey and Outlookcited by 15
  26. 2025Vendi-RAG: Adaptively Trading-Off Diversity And Quality Significantly Improves Retrieval Augmented Generation With LLMscited by 13
  27. 2024Delving into the Reversal Curse: How Far Can Large Language Models Generalize?cited by 11
  28. 2024Chain of Evidences and Evidence to Generate: Prompting for Context Grounded and Retrieval Augmented Reasoningcited by 7
  29. 2024Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learningcited by 7
  30. 2023Allies: Prompting Large Language Model with Beam Searchcited by 6
  31. 2023Large Language Models as Source Planner for Personalized Knowledge-grounded Dialoguecited by 6
  32. 2023Hint-enhanced In-Context Learning wakes Large Language Models up for knowledge-intensive taskscited by 6
  33. 2024ImageRAG: Enhancing Ultra High Resolution Remote Sensing Imagery Analysis with ImageRAGcited by 6
  34. 2024Experimenting with Legal AI Solutions: The Case of Question-Answering for Access to Justicecited by 5
  35. 2023KGQuiz: Evaluating the Generalization of Encoded Knowledge in Large Language Modelscited by 4
  36. 2024KS-LLM: Knowledge Selection of Large Language Models with Evidence Document for Question Answeringcited by 4
  37. 2024AT-RAG: An Adaptive RAG Model Enhancing Query Efficiency with Topic Filtering and Iterative Reasoningcited by 3
  38. 2025Leveraging Metamemory Agent for Enhanced Data-Free Code Generation in Large Language Modelscited by 3

50 most cited of 65 All 65 (JSON)

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  1. 2026ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning
  2. 2024Refiner: Restructure Retrieval Content Efficiently to Advance Question-Answering Capabilities
  3. 2024Let your LLM generate a few tokens and you will reduce the need for retrieval
  4. 2025Recitation over Reasoning: How Cutting-Edge Language Models Can Fail on Elementary School-Level Reasoning Problems?
  5. 2025Recursive Language Models
  6. 2023RaLLe: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Models
  7. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Context
  8. 2023Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves