With the proliferation of dialogic data across the Internet, the Dialogue Commonsense Multi-choice Question Answering (DC-MCQ) task has emerged as a response to the challenge of comprehending user queries and intentions. Although prevailing methodologies exhibit effectiveness in addressing single-choice questions, they encounter difficulties in handling multi-choice queries due to the heightened intricacy and informational density. In this paper, inspired by the human cognitive process of progressively excluding options, we propose a three-step Reverse Exclusion Graph-of-Thought (ReX-GoT) framework, including Option Exclusion, Error Analysis, and Combine Information. Specifically, our ReX-GoT mimics human reasoning by gradually excluding irrelevant options and learning the reasons for option errors to choose the optimal path of the GoT and ultimately infer the correct answer. By progressively integrating intricate clues, our method effectively reduces the difficulty of multi-choice reasoning and provides a novel solution for DC-MCQ. Extensive experiments on the CICERO and CICERO$_{v2}$ datasets validate the significant improvement of our approach on DC-MCQ task. On zero-shot setting, our model outperform the best baseline by 17.67% in terms of F1 score for the multi-choice task. Most strikingly, our GPT3.5-based ReX-GoT framework achieves a remarkable 39.44% increase in F1 score.

翻译：随着互联网上对话数据的激增，对话常识多选问答任务应运而生，旨在应对理解用户查询与意图的挑战。尽管现有方法在解决单选题方面表现出有效性，但由于多选问题的高度复杂性和信息密集性，这些方法在处理此类任务时面临困难。受人类通过逐步排除选项的认知过程启发，本文提出了一种三步反向排除思维图框架，包括选项排除、错误分析与信息整合。具体而言，我们的ReX-GoT通过逐步排除无关选项并学习选项错误的原因来模拟人类推理，从而选择思维图中的最优路径，最终推断出正确答案。通过渐进式整合复杂线索，该方法有效降低了多选推理的难度，为DC-MCQ提供了新颖的解决方案。在CICERO和CICERO$_{v2}$数据集上的大量实验验证了我们的方法在DC-MCQ任务上的显著提升。在零样本设置下，我们的模型在多选任务的F1得分上比最优基线高出17.67%。最引人注目的是，基于GPT3.5的ReX-GoT框架实现了惊人的39.44%的F1得分提升。