mlsanityData to 7 Oct 2026

ChatLang-8: An LLM-Based Synthetic Data Generation Framework for Grammatical Error Correction

arXiv 2406.03202 ↗First posted 5 Jun 2024cs.CL cs.AIcited by 2

On the map: Synthetic data and self-improvement for LLMs → LLM synthetic data generation

Abstract (arXiv)

We explore and improve the capabilities of LLMs to generate data for grammatical error correction (GEC). When merely producing parallel sentences, their patterns are too simplistic to be valuable as a corpus. To address this issue, we propose an automated framework that includes a Subject Selector, Grammar Selector, Prompt Manager, and Evaluator. Additionally, we introduce a new dataset for GEC tasks, named ChatLang-8, which encompasses eight types of subject nouns and 23 types of grammar. It consists of 1 million pairs featuring human-like grammatical errors. Our experiments reveal that ChatLang-8 exhibits a more uniform pattern composition compared to existing GEC datasets. Furthermore, we observe improved model performance when using ChatLang-8 instead of existing GEC datasets. The experimental results suggest that our framework and ChatLang-8 are valuable resources for enhancing ChatGPT's data generation capabilities.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 12 of its 34 references that are in the map. First posted on arXiv 5 Jun 2024.

Builds on

12 of 34 references.

References · 12

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  3. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 888
  4. 2020GECToR -- Grammatical Error Correction: Tag, Not Rewritecited by 177
  5. 2023Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarizationcited by 122
  6. 2021A Simple Recipe for Multilingual Grammatical Error Correctioncited by 108
  7. 2023Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluationcited by 97
  8. 2023ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmarkcited by 77
  9. 2021Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Modelscited by 56
  10. 2019Corpora Generation for Grammatical Error Correctioncited by 53
  11. 2020Data Weighted Training Strategies for Grammatical Error Correctioncited by 26
  12. 2021Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correctioncited by 19

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024Towards a Theoretical Understanding of Synthetic Data in LLM Post-Training: A Reverse-Bottleneck Perspectivecited by 15
  2. 2026RoLegalGEC: Legal Domain Grammatical Error Detection and Correction Dataset for Romaniancited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Zero-shot Cross-Lingual Transfer for Synthetic Data Generation in Grammatical Error Detection
  2. 2024Leveraging LLMs for Bangla Grammar Error Correction:Error Categorization, Synthetic Data, and Model Evaluation
  3. 2024Evaluating Language Models as Synthetic Data Generators
  4. 2026A Factorial Study of Synthetic Data Generation for Low-Resource Machine Translation using Grammar Books
  5. 2024A Survey on Data Synthesis and Augmentation for Large Language Models
  6. 2024Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models
  7. 2024CodecLM: Aligning Language Models with Tailored Synthetic Data
  8. 2025Modular Techniques for Synthetic Long-Context Data Generation in Language Model Training and Evaluation