mlsanityData to 7 Oct 2026

Learning to Represent Edits

arXiv 1810.13337 ↗First posted 31 Oct 2018cs.LG cs.SE stat.MLcited by 34

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Abstract (arXiv)

We introduce the problem of learning distributed representations of edits. By combining a "neural editor" with an "edit encoder", our models learn to represent the salient information of an edit and can be used to apply edits to new inputs. We experiment on natural language and source code edit data. Our evaluation yields promising results that suggest that our neural network models learn to capture the structure and semantics of edits. We hope that this interesting task and data source will inspire other researchers to work further on this problem.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Oct 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 8; 2023: 9; 2024: 3; 2025: 1; 2026: 2.

34 papers.

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  1. 2024Aligning LLM Agents by Learning Latent Preference from User Editscited by 66
  2. 2022CoditT5: Pretraining for Source Code and Natural Language Editingcited by 45
  3. 2022DiffusER: Discrete Diffusion via Edit-based Reconstructioncited by 41
  4. 2021Neural Transfer Learning for Repairing Security Vulnerabilities in C Codecited by 36
  5. 2022Learning to Model Editing Processescited by 30
  6. 2023InstructCoder: Instruction Tuning Large Language Models for Code Editingcited by 28
  7. 2021Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Modelscited by 23
  8. 2023InstructABSA: Instruction Learning for Aspect Based Sentiment Analysiscited by 22
  9. 2021VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatementscited by 20
  10. 2022NewsEdits: A News Article Revision Dataset and a Document-Level Reasoning Challengecited by 20
  11. 2021Learning Structural Edits via Incremental Tree Transformationscited by 16
  12. 2023GrACE: Generation using Associated Code Editscited by 16
22 more
  1. 2022On Distribution Shift in Learning-based Bug Detectorscited by 15
  2. 2023KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repaircited by 13
  3. 2021Context-NER : Contextual Phrase Generation at Scalecited by 11
  4. 2021VarCLR: Variable Semantic Representation Pre-training via Contrastive Learningcited by 11
  5. 2022Fix Bugs with Transformer through a Neural-Symbolic Edit Grammarcited by 8
  6. 2022Overwatch: Learning Patterns in Code Edit Sequencescited by 8
  7. 2021Controlling Text Edition by Changing Answers of Specific Questionscited by 6
  8. 2023XATU: A Fine-grained Instruction-based Benchmark for Explainable Text Updatescited by 6
  9. 2021Unsupervised Learning of General-Purpose Embeddings for Code Changescited by 3
  10. 2023CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Backcited by 3
  11. 2023EDM3: Event Detection as Multi-task Text Generationcited by 3
  12. 2021NewsEdits: A Dataset of Revision Histories for News Articles (Technical Report: Data Processing)cited by 2
  13. 2023Interactive Text Generationcited by 2
  14. 2021Assessing the Effectiveness of Syntactic Structure to Learn Code Edit Representationscited by 1
  15. 2022LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognitioncited by 1
  16. 2023Implant Global and Local Hierarchy Information to Sequence based Code Representation Modelscited by 1
  17. 2024Just-In-Time Software Defect Prediction via Bi-modal Change Representation Learningcited by 1
  18. 2025Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networkscited by 1
  19. 2021Graph Neural Networks for Natural Language Processing: A Surveycited by 0
  20. 2024Analyzing the Evolution of Graphs and Textscited by 0
  21. 2026Principled Fine-tuning of LLMs from User-Edits: A Medley of Preference, Supervision, and Rewardcited by 0
  22. 2026SPHERE: Adaptive VR Indoor Scene Generation via LLM-Enhanced Spatial Preference Learning and Human-in-the-Loop RLcited by 0