  Most language models currently available are prone to self-contradiction
during dialogues. To mitigate this issue, this study explores a novel
contradictory dialogue processing task that aims to detect and modify
contradictory statements in a conversation. This task is inspired by research
on context faithfulness and dialogue comprehension, which have demonstrated
that the detection and understanding of contradictions often necessitate
detailed explanations. We develop a dataset comprising contradictory dialogues,
in which one side of the conversation contradicts itself. Each dialogue is
accompanied by an explanatory label that highlights the location and details of
the contradiction. With this dataset, we present a Red Teaming framework for
contradictory dialogue processing. The framework detects and attempts to
explain the dialogue, then modifies the existing contradictory content using
the explanation. Our experiments demonstrate that the framework improves the
ability to detect contradictory dialogues and provides valid explanations.
Additionally, it showcases distinct capabilities for modifying such dialogues.
Our study highlights the importance of the logical inconsistency problem in
conversational AI.
