Eval-GCSC: A New Metric for Evaluating ChatGPT's Performance in Chinese
  Spelling Correction
Kunting Li Yong Hu Shaolei Wang Hanhan Ma Liang He Fandong Meng Jie Zhou
ChatGPT has demonstrated impressive performance in various downstream tasks.
However, in the Chinese Spelling Correction (CSC) task, we observe a
discrepancy: while ChatGPT performs well under human evaluation, it scores
poorly according to traditional metrics. We believe this inconsistency arises
because the traditional metrics are not well-suited for evaluating generative
models. Their overly strict length and phonics constraints may lead to
underestimating ChatGPT's correction capabilities. To better evaluate
generative models in the CSC task, this paper proposes a new evaluation metric:
Eval-GCSC. By incorporating word-level and semantic similarity judgments, it
relaxes the stringent length and phonics constraints. Experimental results show
that Eval-GCSC closely aligns with human evaluations. Under this metric,
ChatGPT's performance is comparable to traditional token-level classification
models (TCM), demonstrating its potential as a CSC tool. The source code and
scripts can be accessed at https://github.com/ktlKTL/Eval-GCSC.