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Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Models

23 Apr 2024arXiv:2404.14914archive 2025-07-28

Kostiantyn Omelianchuk, Andrii Liubonko, Oleksandr Skurzhanskyi, Artem Chernodub, Oleksandr Korniienko, Igor Samokhin

In this paper, we carry out experimental research on Grammatical Error Correction, delving into the nuances of single-model systems, comparing the efficiency of ensembling and ranking methods, and exploring the application of large language models to GEC as single-model systems, as parts of ensembles, and as ranking methods. We set new state-of-the-art performance with F_0.5 scores of 72.8 on CoNLL-2014-test and 81.4 on BEA-test, respectively. To support further advancements in GEC and ensure the reproducibility of our research, we make our code, trained models, and systems' outputs publicly available.

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Code

grammarly/pillars-of-gec officialmentioned in papermentioned on GitHubpytorch report

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Grammatical Error Correction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Grammatical Error Correction BEA-2019 (test) Majority-voting ensemble on best 7 models F0.5 81.4 #1 of 19 Archive leaderboard report
Grammatical Error Correction CoNLL-2014 Shared Task Ensembles of best 7 models + GRECO + GTP-rerank F0.5 72.8 #1 of 23 Archive leaderboard report
Grammatical Error Correction CoNLL-2014 Shared Task Ensembles of best 7 models + GRECO + GTP-rerank Precision 83.9 #1 of 23 Archive leaderboard report
Grammatical Error Correction CoNLL-2014 Shared Task Ensembles of best 7 models + GRECO + GTP-rerank Recall 47.5 #1 of 23 Archive leaderboard report
Grammatical Error Correction CoNLL-2014 Shared Task Majority-voting ensemble on best 7 models F0.5 71.8 #2 of 23 Archive leaderboard report
Grammatical Error Correction CoNLL-2014 Shared Task Majority-voting ensemble on best 7 models Precision 83.7 #2 of 23 Archive leaderboard report
Grammatical Error Correction CoNLL-2014 Shared Task Majority-voting ensemble on best 7 models Recall 45.7 #2 of 23 Archive leaderboard report

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Methods

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