# GEE! Grammar Error Explanation with Large Language Models

arXiv: https://arxiv.org/abs/2311.09517 · HTML: https://mlsanity.com/paper/2311.09517/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 74 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [GECToR -- Grammatical Error Correction: Tag, Not Rewrite](https://mlsanity.com/paper/2005.12592/) (2020)
- [Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature](https://mlsanity.com/paper/2210.14250/) (2022)
- [Enhancing Grammatical Error Correction Systems with Explanations](https://mlsanity.com/paper/2305.15676/) (2023)
- [FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2210.12364/) (2022)
- [Sentence-level Feedback Generation for English Language Learners: Does Data Augmentation Help?](https://mlsanity.com/paper/2212.08999/) (2022)

## Built on by (most cited first, 15 of 15)

- [LLMs can Perform Multi-Dimensional Analytic Writing Assessments: A Case Study of L2 Graduate-Level Academic English Writing](https://mlsanity.com/paper/2502.11368/) (2025)
- [Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction](https://mlsanity.com/paper/2402.11420/) (2024)
- [Grammatical Error Feedback: An Implicit Evaluation Approach](https://mlsanity.com/paper/2408.09565/) (2024)
- [Scaling and Prompting for Improved End-to-End Spoken Grammatical Error Correction](https://mlsanity.com/paper/2505.21137/) (2025)
- [Annotating Errors in English Learners' Written Language Production: Advancing Automated Written Feedback Systems](https://mlsanity.com/paper/2508.06810/) (2025)
- [Explanation based In-Context Demonstrations Retrieval for Multilingual Grammatical Error Correction](https://mlsanity.com/paper/2502.08507/) (2025)
- [End-to-End Spoken Grammatical Error Correction](https://mlsanity.com/paper/2506.18532/) (2025)
- [MetricalARGS: A Taxonomy for Studying Metrical Poetry with LLMs](https://mlsanity.com/paper/2510.08188/) (2025)
- [An Analysis of Language Frequency and Error Correction for Esperanto](https://mlsanity.com/paper/2402.09696/) (2024)
- [Leveraging Prompt-Tuning for Bengali Grammatical Error Explanation Using Large Language Models](https://mlsanity.com/paper/2504.05642/) (2025)
- [RE2: Improving Chinese Grammatical Error Correction via Retrieving Appropriate Examples with Explanation](https://mlsanity.com/paper/2509.26038/) (2025)
- [Exploiting the English Grammar Profile for L2 grammatical analysis with LLMs](https://mlsanity.com/paper/2603.17171/) (2026)
- [ArabiGEE: A Hierarchical Taxonomy for Arabic Grammatical Error Explanation](https://mlsanity.com/paper/2606.10765/) (2026)
- [Encode Errors: Representational Retrieval of In-Context Demonstrations for Multilingual Grammatical Error Correction](https://mlsanity.com/paper/2606.15416/) (2026)
- [How Well Does AI-Generated Feedback Work? Intrinsic and Extrinsic Evaluation across more than 20,000 EFL Essay Drafts](https://mlsanity.com/paper/2607.14591/) (2026)

## Abstract (arXiv)

Grammatical error correction tools are effective at correcting grammatical errors in users' input sentences but do not provide users with textitnatural language explanations about their errors. Such explanations are essential for helping users learn the language by gaining a deeper understanding of its grammatical rules (DeKeyser, 2003; Ellis et al., 2006). To address this gap, we propose the task of grammar error explanation, where a system needs to provide one-sentence explanations for each grammatical error in a pair of erroneous and corrected sentences. We analyze the capability of GPT-4 in grammar error explanation, and find that it only produces explanations for 60.2% of the errors using one-shot prompting. To improve upon this performance, we develop a two-step pipeline that leverages fine-tuned and prompted large language models to perform structured atomic token edit extraction, followed by prompting GPT-4 to generate explanations. We evaluate our pipeline on German and Chinese grammar error correction data sampled from language learners with a wide range of proficiency levels. Human evaluation reveals that our pipeline produces 93.9% and 98.0% correct explanations for German and Chinese data, respectively. To encourage further research in this area, we will open-source our data and code.