  As large language models, such as GPT, continue to advance the capabilities
of natural language processing (NLP), the question arises: does the problem of
correction still persist? This paper investigates the role of correction in the
context of large language models by conducting two experiments. The first
experiment focuses on correction as a standalone task, employing few-shot
learning techniques with GPT-like models for error correction. The second
experiment explores the notion of correction as a preparatory task for other
NLP tasks, examining whether large language models can tolerate and perform
adequately on texts containing certain levels of noise or errors. By addressing
these experiments, we aim to shed light on the significance of correction in
the era of large language models and its implications for various NLP
applications.
