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Found 6 papers, 3 papers with code
Not All Models Localize Linguistic Knowledge in the Same Place: A Layer-wise Probing on BERToids’ Representations
Most of the recent works on probing representations have focused on BERT, with the presumption that the findings might be similar to the other models.
BERT on a Data Diet: Finding Important Examples by Gradient-Based Pruning
In this work, we employ these two metrics for the first time in NLP.
GlobEnc: Quantifying Global Token Attribution by Incorporating the Whole Encoder Layer in Transformers
There has been a growing interest in interpreting the underlying dynamics of Transformers.
AdapLeR: Speeding up Inference by Adaptive Length Reduction
To determine the importance of each token representation, we train a Contribution Predictor for each layer using a gradient-based saliency method.
Not All Models Localize Linguistic Knowledge in the Same Place: A Layer-wise Probing on BERToids' Representations
Most of the recent works on probing representations have focused on BERT, with the presumption that the findings might be similar to the other models.
Exploring the Role of BERT Token Representations to Explain Sentence Probing Results
Several studies have been carried out on revealing linguistic features captured by BERT.
