mlsanityData to 7 Oct 2026

PEER: A Collaborative Language Model

arXiv 2208.11663 ↗First posted 24 Aug 2022cs.CLcited by 86

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Abstract (arXiv)

Textual content is often the output of a collaborative writing process: We start with an initial draft, ask for suggestions, and repeatedly make changes. Agnostic of this process, today's language models are trained to generate only the final result. As a consequence, they lack several abilities crucial for collaborative writing: They are unable to update existing texts, difficult to control and incapable of verbally planning or explaining their actions. To address these shortcomings, we introduce PEER, a collaborative language model that is trained to imitate the entire writing process itself: PEER can write drafts, add suggestions, propose edits and provide explanations for its actions. Crucially, we train multiple instances of PEER able to infill various parts of the writing process, enabling the use of self-training techniques for increasing the quality, amount and diversity of training data. This unlocks PEER's full potential by making it applicable in domains for which no edit histories are available and improving its ability to follow instructions, to write useful comments, and to explain its actions. We show that PEER achieves strong performance across various domains and editing tasks.

Key facts. Cited by 86 papers in the map (data through 7 Oct 2026). Builds on 52 of its 75 references that are in the map. First posted on arXiv 24 Aug 2022.

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52 of 75 references.

References · 52

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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  1. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
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  3. 2022Diffusion-LM Improves Controllable Text Generationcited by 1,006
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  24. 2019Parallel Iterative Edit Models for Local Sequence Transductioncited by 81
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  38. 2022Learning to Model Editing Processescited by 30

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Citing papers per year: 2022: 8; 2023: 43; 2024: 20; 2025: 10; 2026: 5.

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  1. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  2. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  3. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
  4. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
  5. 2023Augmented Language Models: a Surveycited by 398
  6. 2023MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedbackcited by 293
  7. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 286
  8. 2024A Survey on Knowledge Distillation of Large Language Modelscited by 276
  9. 2022Generating Sequences by Learning to Self-Correctcited by 271
  10. 2023Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategiescited by 236
  11. 2024When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMscited by 230
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  3. 2023Training Language Models with Language Feedback at Scalecited by 125
  4. 2023RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputscited by 104
  5. 2024GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinementscited by 101
  6. 2023DRESS: Instructing Large Vision-Language Models to Align and Interact with Humans via Natural Language Feedbackcited by 99
  7. 2023Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extractioncited by 89
  8. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 89
  9. 2024The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Modelscited by 89
  10. 2023ChatGPT is not all you need. A State of the Art Review of large Generative AI modelscited by 85
  11. 2024Feedback Loops With Language Models Drive In-Context Reward Hackingcited by 70
  12. 2023CoEdIT: Text Editing by Task-Specific Instruction Tuningcited by 67
  13. 2023RewriteLM: An Instruction-Tuned Large Language Model for Text Rewritingcited by 67
  14. 2024Agents' Room: Narrative Generation through Multi-step Collaborationcited by 57
  15. 2023Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generationcited by 55
  16. 2023SummIt: Iterative Text Summarization via ChatGPTcited by 53
  17. 2023PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptionscited by 53
  18. 2024Internal Consistency and Self-Feedback in Large Language Models: A Surveycited by 49
  19. 2023LLM-Powered Hierarchical Language Agent for Real-time Human-AI Coordinationcited by 48
  20. 2025A Survey of Large Language Model Agents for Question Answeringcited by 46
  21. 2024DetToolChain: A New Prompting Paradigm to Unleash Detection Ability of MLLMcited by 45
  22. 2025Feedback Descent: Open-Ended Text Optimization via Pairwise Comparisoncited by 42
  23. 2022Nonparametric Masked Language Modelingcited by 39
  24. 2022On Improving Summarization Factual Consistency from Natural Language Feedbackcited by 38
  25. 2023Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methodscited by 37
  26. 2023Beyond the Chat: Executable and Verifiable Text-Editing with LLMscited by 35
  27. 2023Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformerscited by 35
  28. 2023ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviewscited by 34
  29. 2024LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraintscited by 34
  30. 2023Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuningcited by 33
  31. 2022EditEval: An Instruction-Based Benchmark for Text Improvementscited by 30
  32. 2023Learning to Reason and Memorize with Self-Notescited by 30
  33. 2023Zero-shot Faithful Factual Error Correctioncited by 26
  34. 2024Nearest Neighbor Speculative Decoding for LLM Generation and Attributioncited by 26
  35. 2024I Don't Know: Explicit Modeling of Uncertainty with an [IDK] Tokencited by 26
  36. 2023Flows: Building Blocks of Reasoning and Collaborating AIcited by 25
  37. 2024Multi-Agent Large Language Models for Conversational Task-Solvingcited by 24
  38. 2024Small Language Model Can Self-correctcited by 23

50 most cited of 86 All 86 (JSON)