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hello everyone i am puya from uc irvine and today iamp;#39;m going to talk about combining features and incense attribution to detect artifacts is the joint fork by me my advisor sami sin and also sorta jane and byron wallace from north eastern university so what is an artifact artifact is a serious correlation between features in the input and the labels which model exploit to make his prediction example of artifacts in nlp benchmarks are lexical overlap in nli tests which model exploit to mispredict instances as entailments ratings in imdb reviews and also african-american dialects in toxic detection over tweets as you can see there are not many known artifacts in current nlp benchmark and the reason is identifying this artifact is very expensive and hard manual level so the whole goal behind this project is somehow make this process easier the solution that we find for this test was using different attribution methods we know attribution methods highlight important patterns in the