mlsanityData to 7 Oct 2026

Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods

arXiv 2305.18156 ↗First posted 29 May 2023cs.CL cs.AIcited by 37

Not on the map. Why?

Abstract (arXiv)

Large-scale pre-trained language models such as GPT-3 have shown remarkable performance across various natural language processing tasks. However, applying prompt-based methods with GPT-3 for Grammatical Error Correction (GEC) tasks and their controllability remains underexplored. Controllability in GEC is crucial for real-world applications, particularly in educational settings, where the ability to tailor feedback according to learner levels and specific error types can significantly enhance the learning process. This paper investigates the performance and controllability of prompt-based methods with GPT-3 for GEC tasks using zero-shot and few-shot setting. We explore the impact of task instructions and examples on GPT-3's output, focusing on controlling aspects such as minimal edits, fluency edits, and learner levels. Our findings demonstrate that GPT-3 could effectively perform GEC tasks, outperforming existing supervised and unsupervised approaches. We also showed that GPT-3 could achieve controllability when appropriate task instructions and examples are given.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). Builds on 22 of its 52 references that are in the map. First posted on arXiv 29 May 2023.

Builds on

22 of 52 references.

References · 22

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2016Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translationcited by 467
  4. 2021Do Prompt-Based Models Really Understand the Meaning of their Prompts?cited by 324
  5. 2023Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluationcited by 97
  6. 2017JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correctioncited by 96
  7. 2022PEER: A Collaborative Language Modelcited by 86
  8. 2020Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correctioncited by 59
  9. 2018Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Taskcited by 56
  10. 2019An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correctioncited by 55
  11. 2019Corpora Generation for Grammatical Error Correctioncited by 53
  12. 2019Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Datacited by 50
10 more
  1. 2018A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correctioncited by 36
  2. 2021LM-Critic: Language Models for Unsupervised Grammatical Error Correctioncited by 33
  3. 2022EditEval: An Instruction-Based Benchmark for Text Improvementscited by 30
  4. 2019A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learningcited by 26
  5. 2019Improving Grammatical Error Correction with Machine Translation Pairscited by 17
  6. 2020Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analysescited by 16
  7. 2022Interpretability for Language Learners Using Example-Based Grammatical Error Correctioncited by 13
  8. 2017A Nested Attention Neural Hybrid Model for Grammatical Error Correctioncited by 12
  9. 2021Comparison of Grammatical Error Correction Using Back-Translation Modelscited by 9
  10. 2019The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correctioncited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 16; 2025: 10; 2026: 9.

37 papers.

Sort:

By area:

  1. 2024Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMscited by 224
  2. 2024Prompting open-source and commercial language models for grammatical error correction of English learner textcited by 24
  3. 2024Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?cited by 19
  4. 2024GPT-3.5 for Grammatical Error Correctioncited by 18
  5. 2024ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper Pagescited by 10
  6. 2024mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humanscited by 10
  7. 2024Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiencycited by 9
  8. 2025Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Studycited by 9
  9. 2025gec-metrics: A Unified Library for Grammatical Error Correction Evaluationcited by 9
  10. 2024Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Modelscited by 8
  11. 2023Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correctioncited by 7
  12. 2024LLMs in Education: Novel Perspectives, Challenges, and Opportunitiescited by 7
25 more
  1. 2025Rethinking Evaluation Metrics for Grammatical Error Correction: Why Use a Different Evaluation Process than Human?cited by 7
  2. 2023RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbationcited by 6
  3. 2025Opportunities and Challenges of LLMs in Education: An NLP Perspectivecited by 6
  4. 2025Adapting LLMs for Minimal-edit Grammatical Error Correctioncited by 5
  5. 2024Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correctioncited by 2
  6. 2025APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplificationcited by 2
  7. 2026MedErrBench: A Fine-Grained Multilingual Benchmark for Medical Error Detection and Correction with Clinical Expert Annotationscited by 2
  8. 2024Llama-VITS: Enhancing TTS Synthesis with Semantic Awarenesscited by 1
  9. 2024PERCORE: A Deep Learning-Based Framework for Persian Spelling Correction with Phonetic Analysiscited by 1
  10. 2024Tibyan Corpus: Balanced and Comprehensive Error Coverage Corpus Using ChatGPT for Arabic Grammatical Error Correctioncited by 1
  11. 2025Explanation based In-Context Demonstrations Retrieval for Multilingual Grammatical Error Correctioncited by 1
  12. 2026Edit-level Majority Voting Mitigates Over-Correction in LLM-based Grammatical Error Correctioncited by 1
  13. 2026How Far Can Prompting Go for Minimal-Edit Ukrainian Grammatical Error Correction?cited by 1
  14. 2024Neural Automated Writing Evaluation with Corrective Feedbackcited by 0
  15. 2024Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correctioncited by 0
  16. 2024A Simple Yet Effective Corpus Construction Framework for Indonesian Grammatical Error Correctioncited by 0
  17. 2025Integration of LLM Quality Assurance into an NLG Systemcited by 0
  18. 2025Joint Extraction Matters: Prompt-Based Visual Question Answering for Multi-Field Document Information Extractioncited by 0
  19. 2025Auto Review: Second Stage Error Detection for Highly Accurate Information Extraction from Phone Conversationscited by 0
  20. 2026MC-PDD: Masked Corpus-Level Pretraining Data Detection for Black-Box Large Language Modelscited by 0
  21. 2026CoCoGEC: Counterfactual Generation for Robust Grammatical Error Correctioncited by 0
  22. 2026Encode Errors: Representational Retrieval of In-Context Demonstrations for Multilingual Grammatical Error Correctioncited by 0
  23. 2026STAGEET: Stage-wise Typed Edit Tagging for Grammatical Error Correction with Arabic as a Case Studycited by 0
  24. 2026Larger Context Window, Fewer Overcorrections: Optimizing Prompts and Batching for Minimal-Edit Grammatical Error Correctioncited by 0
  25. 2026Which LLM to pick? Online Active Model Selection for Large Language Modelscited by 0