# GLEU Without Tuning

arXiv: https://arxiv.org/abs/1605.02592 · HTML: https://mlsanity.com/paper/1605.02592/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2016.

## Built on by (most cited first, 24 of 24)

- [Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks](https://mlsanity.com/paper/2107.07455/) (2021)
- [Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation](https://mlsanity.com/paper/2304.01746/) (2023)
- [Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond](https://mlsanity.com/paper/2205.11484/) (2022)
- [Think Twice: A Human-like Two-stage Conversational Agent for Emotional Response Generation](https://mlsanity.com/paper/2301.04907/) (2023)
- [mEdIT: Multilingual Text Editing via Instruction Tuning](https://mlsanity.com/paper/2402.16472/) (2024)
- [Comparison of Grammatical Error Correction Using Back-Translation Models](https://mlsanity.com/paper/2104.07848/) (2021)
- [CrowdSpeech and VoxDIY: Benchmark Datasets for Crowdsourced Audio Transcription](https://mlsanity.com/paper/2107.01091/) (2021)
- [Reference-free Evaluation Metrics for Text Generation: A Survey](https://mlsanity.com/paper/2501.12011/) (2025)
- [gec-metrics: A Unified Library for Grammatical Error Correction Evaluation](https://mlsanity.com/paper/2505.19388/) (2025)
- [Byte-Level Grammatical Error Correction Using Synthetic and Curated Corpora](https://mlsanity.com/paper/2305.17906/) (2023)
- [Rethinking Evaluation Metrics for Grammatical Error Correction: Why Use a Different Evaluation Process than Human?](https://mlsanity.com/paper/2502.09416/) (2025)
- [Automated Query Reformulation for Efficient Search based on Query Logs From Stack Overflow](https://mlsanity.com/paper/2102.00826/) (2021)
- [Revisiting Meta-evaluation for Grammatical Error Correction](https://mlsanity.com/paper/2403.02674/) (2024)
- [Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correction](https://mlsanity.com/paper/2403.17540/) (2024)
- [Introducing OmniGEC: A Silver Multilingual Dataset for Grammatical Error Correction](https://mlsanity.com/paper/2509.14504/) (2025)
- [Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting](https://mlsanity.com/paper/2310.13321/) (2023)
- [Multi-Dimensional Evaluation of LLMs for Grammatical Error Correction](https://mlsanity.com/paper/2605.07635/) (2026)
- [Edit-level Majority Voting Mitigates Over-Correction in LLM-based Grammatical Error Correction](https://mlsanity.com/paper/2605.13624/) (2026)
- [Time Is Effort: Estimating Human Post-Editing Time for Grammar Error Correction Tool Evaluation](https://mlsanity.com/paper/2510.04394/) (2025)
- [A Formal Framework for Fluency-based Multi-Reference Evaluation in Grammatical Error Correction](https://mlsanity.com/paper/2510.06749/) (2025)
- [Minimal-Edit Instruction Tuning for Low-Resource Indic GEC](https://mlsanity.com/paper/2512.00219/) (2025)
- [Grammatical Error Correction Evaluation by Optimally Transporting Edit Representation](https://mlsanity.com/paper/2602.05419/) (2026)
- [Refining Word-Based Grammatical Error Annotation for L2 Korean](https://mlsanity.com/paper/2605.30545/) (2026)
- [WrAFT: a Modularized Automated Writing Evaluation System for Argumentative Essays](https://mlsanity.com/paper/2607.14524/) (2026)

## Abstract (arXiv)

The GLEU metric was proposed for evaluating grammatical error corrections using n-gram overlap with a set of reference sentences, as opposed to precision/recall of specific annotated errors (Napoles et al., 2015). This paper describes improvements made to the GLEU metric that address problems that arise when using an increasing number of reference sets. Unlike the originally presented metric, the modified metric does not require tuning. We recommend that this version be used instead of the original version.