  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.
