In this paper, we assess the robustness (reliability) of ChatGPT under input
perturbations for one of the most fundamental tasks of Information Extraction
(IE) i.e. Named Entity Recognition (NER). Despite the hype, the majority of the
researchers have vouched for its language understanding and generation
capabilities; a little attention has been paid to understand its robustness:
How the input-perturbations affect 1) the predictions, 2) the confidence of
predictions and 3) the quality of rationale behind its prediction. We perform a
systematic analysis of ChatGPT's robustness (under both zero-shot and few-shot
setup) on two NER datasets using both automatic and human evaluation. Based on
automatic evaluation metrics, we find that 1) ChatGPT is more brittle on Drug
or Disease replacements (rare entities) compared to the perturbations on widely
known Person or Location entities, 2) the quality of explanations for the same
entity considerably differ under different types of "Entity-Specific" and
"Context-Specific" perturbations and the quality can be significantly improved
using in-context learning, and 3) it is overconfident for majority of the
incorrect predictions, and hence it could lead to misguidance of the end-users.

通过对两个命名实体识别数据集进行自动和人工评估的系统分析，发现 ChatGPT 在一些少见的实体替换（如药物或疾病）方面表现相对脆弱，不同类型的实体特定和上下文特定扰动导致相同实体的解释质量有显著差异且可通过上下文学习显著改善，并且对于大多数不正确的预测过于自信，可能给最终用户带来错误引导。