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With the proliferation of dialogic data across the Internet, the DialogueCommonsense Multi-choice Question Answering (DC-MCQ) task has emerged as aresponse to the challenge of comprehending user queries and intentions.Although prevailing methodologies exhibit effectiveness in addressingsingle-choice questions, they encounter difficulties in handling multi-choicequeries due to the heightened intricacy and informational density. In thispaper, inspired by the human cognitive process of progressively excludingoptions, 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 excludingirrelevant options and learning the reasons for option errors to choose theoptimal path of the GoT and ultimately infer the correct answer. Byprogressively integrating intricate clues, our method effectively reduces thedifficulty of multi-choice reasoning and provides a novel solution for DC-MCQ.Extensive experiments on the CICERO and CICERO$_{v2}$ datasets validate thesignificant 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 themulti-choice task. Most strikingly, our GPT3.5-based ReX-GoT framework achievesa remarkable 39.44% increase in F1 score.

Reverse Multi-Choice Dialogue Commonsense Inference with Graph-of-Thought