On Identifiability in Transformers
This work addresses interpretability issues in Transformer models, which is crucial for researchers and practitioners in NLP and AI, though it is incremental in nature.
The paper investigates the identifiability of attention weights and token embeddings in Transformers, showing that attention weights are not identifiable for long sequences and proposing effective attention as a tool for better interpretability.
In this paper we delve deep in the Transformer architecture by investigating two of its core components: self-attention and contextual embeddings. In particular, we study the identifiability of attention weights and token embeddings, and the aggregation of context into hidden tokens. We show that, for sequences longer than the attention head dimension, attention weights are not identifiable. We propose effective attention as a complementary tool for improving explanatory interpretations based on attention. Furthermore, we show that input tokens retain to a large degree their identity across the model. We also find evidence suggesting that identity information is mainly encoded in the angle of the embeddings and gradually decreases with depth. Finally, we demonstrate strong mixing of input information in the generation of contextual embeddings by means of a novel quantification method based on gradient attribution. Overall, we show that self-attention distributions are not directly interpretable and present tools to better understand and further investigate Transformer models.