  Event temporal reasoning aims at identifying the temporal relations between two or more events. However, knowledge conflicts arise when there is a mismatch between the actual temporal relations of events in the context and the prior knowledge or biases learned by the model. We first systematically define distinct kinds of bias in event temporal reasoning, which include event relation prior bias, tense bias, narrative bias, and dependency bias, as indicators to study knowledge conflicts. To mitigate such event-related knowledge conflict, we introduce a Counterfactual Data Augmentation based method that can be applied to both Pre-trained Language Models (PLMs) and Large Language Models (LLMs) either as additional training data or demonstrations for In-Context Learning. Experiments suggest the importance of mitigating knowledge conflicts in event temporal reasoning tasks for reducing hallucination and highlight the potential of counterfactual data augmentation for improving model performance. 
Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge   Conflicts in Event Temporal Reasoning