  The ability to identify influential training examples enables us to debug
training data and explain model behavior. Existing techniques to do so are
based on the flow of training data influence through the model parameters. For
large models in NLP applications, it is often computationally infeasible to
study this flow through all model parameters, therefore techniques usually pick
the last layer of weights. However, we observe that since the activation
connected to the last layer of weights contains "shared logic", the data
influenced calculated via the last layer weights prone to a ``cancellation
effect'', where the data influence of different examples have large magnitude
that contradicts each other. The cancellation effect lowers the discriminative
power of the influence score, and deleting influential examples according to
this measure often does not change the model's behavior by much. To mitigate
this, we propose a technique called TracIn-WE that modifies a method called
TracIn to operate on the word embedding layer instead of the last layer, where
the cancellation effect is less severe. One potential concern is that influence
based on the word embedding layer may not encode sufficient high level
information. However, we find that gradients (unlike embeddings) do not suffer
from this, possibly because they chain through higher layers. We show that
TracIn-WE significantly outperforms other data influence methods applied on the
last layer significantly on the case deletion evaluation on three language
classification tasks for different models. In addition, TracIn-WE can produce
scores not just at the level of the overall training input, but also at the
level of words within the training input, a further aid in debugging.
