  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.
