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The ability to identify and control different kinds of linguistic informationencoded in vector representations of words has many use cases, especially forexplainability and bias removal. This is usually done via a set of simpleclassification tasks, termed probes, to evaluate the information encoded in theembedding space. However, the involvement of a trainable classifier leads toentanglement between the probe's results and the classifier's nature. As aresult, contemporary works on probing include tasks that do not involvetraining of auxiliary models. In this work we introduce the term indicatortasks for non-trainable tasks which are used to query embedding spaces for theexistence of certain properties, and claim that this kind of tasks may point toa direction opposite to probes, and that this contradiction complicates thedecision on whether a property exists in an embedding space. We demonstrate ourclaims with two test cases, one dealing with gender debiasing and another withthe erasure of morphological information from embedding spaces. We show thatthe application of a suitable indicator provides a more accurate picture of theinformation captured and removed compared to probes. We thus conclude thatindicator tasks should be implemented and taken into consideration wheneliciting information from embedded representations.

Is Probing All You Need? Indicator Tasks as an Alternative to Probing Embedding Spaces