# GPT-3.5 for Grammatical Error Correction

arXiv: https://arxiv.org/abs/2405.08469 · HTML: https://mlsanity.com/paper/2405.08469/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 50 references that are in the map.
- First posted on arXiv 14 May 2024.

## Builds on (most cited first, 29 of 29)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies](https://mlsanity.com/paper/1611.01368/) (2016)
- [Assessing BERT's Syntactic Abilities](https://mlsanity.com/paper/1901.05287/) (2019)
- [GECToR -- Grammatical Error Correction: Tag, Not Rewrite](https://mlsanity.com/paper/2005.12592/) (2020)
- [A Simple Recipe for Multilingual Grammatical Error Correction](https://mlsanity.com/paper/2106.03830/) (2021)
- [Encode, Tag, Realize: High-Precision Text Editing](https://mlsanity.com/paper/1909.01187/) (2019)
- [JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction](https://mlsanity.com/paper/1702.04066/) (2017)
- [Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation](https://mlsanity.com/paper/2304.01746/) (2023)
- [ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark](https://mlsanity.com/paper/2303.13648/) (2023)
- [Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction](https://mlsanity.com/paper/2005.00987/) (2020)
- [Seq2Edits: Sequence Transduction Using Span-level Edit Operations](https://mlsanity.com/paper/2009.11136/) (2020)
- [Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task](https://mlsanity.com/paper/1804.05940/) (2018)
- [An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction](https://mlsanity.com/paper/1909.00502/) (2019)
- [Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data](https://mlsanity.com/paper/1903.00138/) (2019)
- [Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods](https://mlsanity.com/paper/2305.18156/) (2023)
- [A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction](https://mlsanity.com/paper/1801.08831/) (2018)
- [LM-Critic: Language Models for Unsupervised Grammatical Error Correction](https://mlsanity.com/paper/2109.06822/) (2021)
- [Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance](https://mlsanity.com/paper/2305.13225/) (2023)
- [Grammatical Error Correction in Low-Resource Scenarios](https://mlsanity.com/paper/1910.00353/) (2019)
- [A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learning](https://mlsanity.com/paper/1907.01256/) (2019)
- [Data Weighted Training Strategies for Grammatical Error Correction](https://mlsanity.com/paper/2008.02976/) (2020)
- [UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language](https://mlsanity.com/paper/2103.16997/) (2021)
- [A Nested Attention Neural Hybrid Model for Grammatical Error Correction](https://mlsanity.com/paper/1707.02026/) (2017)
- [Grammatical Error Correction and Style Transfer via Zero-shot Monolingual Translation](https://mlsanity.com/paper/1903.11283/) (2019)
- [Grammatical Error Correction as GAN-like Sequence Labeling](https://mlsanity.com/paper/2105.14209/) (2021)
- [Adversarial Grammatical Error Correction](https://mlsanity.com/paper/2010.02407/) (2020)

## Built on by (most cited first, 18 of 18)

- [Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study](https://mlsanity.com/paper/2503.00985/) (2025)
- [LLMs in Education: Novel Perspectives, Challenges, and Opportunities](https://mlsanity.com/paper/2409.11917/) (2024)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [Personalized Query Auto-Completion for Long and Short-Term Interests with Adaptive Detoxification Generation](https://mlsanity.com/paper/2505.20966/) (2025)
- [Grammatical Error Feedback: An Implicit Evaluation Approach](https://mlsanity.com/paper/2408.09565/) (2024)
- [Exploring the Feasibility of Multilingual Grammatical Error Correction with a Single LLM up to 9B parameters: A Comparative Study of 17 Models](https://mlsanity.com/paper/2505.06004/) (2025)
- [Comparing human and LLM proofreading in L2 writing: Impact on lexical and syntactic features](https://mlsanity.com/paper/2506.09021/) (2025)
- [APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification](https://mlsanity.com/paper/2508.09378/) (2025)
- [Leveraging What's Overfixed: Post-Correction via LLM Grammatical Error Overcorrection](https://mlsanity.com/paper/2509.20811/) (2025)
- [Edit-level Majority Voting Mitigates Over-Correction in LLM-based Grammatical Error Correction](https://mlsanity.com/paper/2605.13624/) (2026)
- [How Far Can Prompting Go for Minimal-Edit Ukrainian Grammatical Error Correction?](https://mlsanity.com/paper/2606.09334/) (2026)
- [Time Is Effort: Estimating Human Post-Editing Time for Grammar Error Correction Tool Evaluation](https://mlsanity.com/paper/2510.04394/) (2025)
- [When the Gold Standard Isn't Necessarily Standard: Challenges of Evaluating the Translation of User-Generated Content](https://mlsanity.com/paper/2512.17738/) (2025)
- [Grammatical Error Correction Evaluation by Optimally Transporting Edit Representation](https://mlsanity.com/paper/2602.05419/) (2026)
- [Exploiting the English Grammar Profile for L2 grammatical analysis with LLMs](https://mlsanity.com/paper/2603.17171/) (2026)
- [CoCoGEC: Counterfactual Generation for Robust Grammatical Error Correction](https://mlsanity.com/paper/2606.15069/) (2026)
- [Larger Context Window, Fewer Overcorrections: Optimizing Prompts and Batching for Minimal-Edit Grammatical Error Correction](https://mlsanity.com/paper/2609.10810/) (2026)
- [Don't Count the Edits, Judge by the Outcome Alone: Reward-Based Evaluation for Grammatical Error Correction](https://mlsanity.com/paper/2609.15559/) (2026)

## Abstract (arXiv)

This paper investigates the application of GPT-3.5 for Grammatical Error Correction (GEC) in multiple languages in several settings: zero-shot GEC, fine-tuning for GEC, and using GPT-3.5 to re-rank correction hypotheses generated by other GEC models. In the zero-shot setting, we conduct automatic evaluations of the corrections proposed by GPT-3.5 using several methods: estimating grammaticality with language models (LMs), the Scribendi test, and comparing the semantic embeddings of sentences. GPT-3.5 has a known tendency to over-correct erroneous sentences and propose alternative corrections. For several languages, such as Czech, German, Russian, Spanish, and Ukrainian, GPT-3.5 substantially alters the source sentences, including their semantics, which presents significant challenges for evaluation with reference-based metrics. For English, GPT-3.5 demonstrates high recall, generates fluent corrections, and generally preserves sentence semantics. However, human evaluation for both English and Russian reveals that, despite its strong error-detection capabilities, GPT-3.5 struggles with several error types, including punctuation mistakes, tense errors, syntactic dependencies between words, and lexical compatibility at the sentence level.