Pre-trained large language models, such as ChatGPT, archive outstanding
performance in various reasoning tasks without supervised training and were
found to have outperformed crowdsourcing workers. Nonetheless, ChatGPT's
performance in the task of implicit discourse relation classification, prompted
by a standard multiple-choice question, is still far from satisfactory and
considerably inferior to state-of-the-art supervised approaches. This work
investigates several proven prompting techniques to improve ChatGPT's
recognition of discourse relations. In particular, we experimented with
breaking down the classification task that involves numerous abstract labels
into smaller subtasks. Nonetheless, experiment results show that the inference
accuracy hardly changes even with sophisticated prompt engineering, suggesting
that implicit discourse relation classification is not yet resolvable under
zero-shot or few-shot settings.

对于 ChatGPT 而言，在隐性话语关系分类任务中，使用标准的多项选择问题提示尚不令人满意，且远远不及最先进的有监督方法。本研究通过尝试多种证实的提示技术来提高 ChatGPT 对话语关系的识别能力，但实验结果表明，即使使用复杂的提示工程，推理准确性几乎没有改变，暗示隐性话语关系分类在零样本或少样本情况下尚未得到解决。