  The rapid advancement of Large Language Models (LLMs) in the realm of
mathematical reasoning necessitates comprehensive evaluations to gauge progress
and inspire future directions. Existing assessments predominantly focus on
problem-solving from the examinee perspective, overlooking a dual perspective
of examiner regarding error identification and correction. From the examiner
perspective, we define four evaluation tasks for error identification and
correction along with a new dataset with annotated error types and steps. We
also design diverse prompts to thoroughly evaluate eleven representative LLMs.
Our principal findings indicate that GPT-4 outperforms all models, while
open-source model LLaMA-2-7B demonstrates comparable abilities to closed-source
models GPT-3.5 and Gemini Pro. Notably, calculation error proves the most
challenging error type. Moreover, prompting LLMs with the error types can
improve the average correction accuracy by 47.9\%. These results reveal
potential directions for developing the mathematical reasoning abilities of
LLMs. Our code and dataset is available on https://github.com/LittleCirc1e/EIC.
