  The automatic evaluation of natural language generation (NLG) systems
presents a long-lasting challenge. Recent studies have highlighted various
neural metrics that align well with human evaluations. Yet, the robustness of
these evaluators against adversarial perturbations remains largely
under-explored due to the unique challenges in obtaining adversarial data for
different NLG evaluation tasks. To address the problem, we introduce AdvEval, a
novel black-box adversarial framework against NLG evaluators. AdvEval is
specially tailored to generate data that yield strong disagreements between
human and victim evaluators. Specifically, inspired by the recent success of
large language models (LLMs) in text generation and evaluation, we adopt strong
LLMs as both the data generator and gold evaluator. Adversarial data are
automatically optimized with feedback from the gold and victim evaluator. We
conduct experiments on 12 victim evaluators and 11 NLG datasets, spanning tasks
including dialogue, summarization, and question evaluation. The results show
that AdvEval can lead to significant performance degradation of various victim
metrics, thereby validating its efficacy.
