English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Large-scale pre-trained language models such as GPT-3 have shown remarkableperformance across various natural language processing tasks. However, applyingprompt-based methods with GPT-3 for Grammatical Error Correction (GEC) tasksand their controllability remains underexplored. Controllability in GEC iscrucial for real-world applications, particularly in educational settings,where the ability to tailor feedback according to learner levels and specificerror types can significantly enhance the learning process. This paperinvestigates the performance and controllability of prompt-based methods withGPT-3 for GEC tasks using zero-shot and few-shot setting. We explore the impactof task instructions and examples on GPT-3's output, focusing on controllingaspects such as minimal edits, fluency edits, and learner levels. Our findingsdemonstrate that GPT-3 could effectively perform GEC tasks, outperformingexisting supervised and unsupervised approaches. We also showed that GPT-3could achieve controllability when appropriate task instructions and examplesare given.

Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods