Evaluating the Capability of Large-scale Language Models on Chinese Grammatical Error Correction Task
This work addresses the problem of applying LLMs to Chinese grammatical error correction for NLP researchers, but it is incremental as it extends prior findings from English to Chinese tasks.
The study evaluated large-scale language models (LLMs) on Chinese grammatical error correction tasks, finding that they underperform state-of-the-art models due to over-correction issues and show performance variations across different data distributions.
Large-scale language models (LLMs) has shown remarkable capability in various of Natural Language Processing (NLP) tasks and attracted lots of attention recently. However, some studies indicated that large language models fail to achieve promising result beyond the state-of-the-art models in English grammatical error correction (GEC) tasks. In this report, we aim to explore the how large language models perform on Chinese grammatical error correction tasks and provide guidance for future work. We conduct experiments with 3 different LLMs of different model scale on 4 Chinese GEC dataset. Our experimental results indicate that the performances of LLMs on automatic evaluation metrics falls short of the previous sota models because of the problem of over-correction. Furthermore, we also discover notable variations in the performance of LLMs when evaluated on different data distributions. Our findings demonstrates that further investigation is required for the application of LLMs on Chinese GEC task.