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 that they are more familiar with due to higher exposure in the training corpus%2C and be more easily persuaded by some contexts than others. To formalize this problem%2C we propose two mutual information-based metrics to measure a model's dependency on a context and on its prior about an entity: first%2C the persuasion score of a given context represents how much a model depends on the context in its decision%2C and second%2C the susceptibility score of a given entity represents how much the model can be swayed away from its original answer distribution about an entity. We empirically test our metrics for their validity and reliability. Finally%2C we explore and find a relationship between the scores and the model's expected familiarity with an entity%2C and provide two use cases to illustrate their benefits.?quality=80&w=800)
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