Evaluating LLMs' grammatical error correction performance in learner Chinese.

ABSTRACT: Large language models (LLMs) have recently exhibited significant capabilities in various English NLP tasks. However, their performance in Chinese grammatical error correction (CGEC) remains unexplored. This study evaluates the abilities of state-of-the-art LLMs in correcting learner Chinese errors from a corpus linguistic perspective. The performance of LLMs is assessed using standard evaluation metrics of MaxMatch score. Keyword and key n-gram analyses are conducted to quantitatively explore linguistic features that differentiate LLM outputs from those of human annotators. LLMs' performance in syntactic and semantic dimensions is further qualitatively analyzed based on these probes of keywords and key n-grams. Results show that LLMs achieve a relatively higher performance in test datasets

SUBMITTER: Lin S 

PROVIDER: S-EPMC11524451 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature