exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models
This tool helps researchers and practitioners in NLP gain intuition into model-internal reasoning, but it is incremental as it builds on existing static analysis methods.
The authors tackled the problem of exploring learned representations in transformer models by developing exBERT, an interactive visual analysis tool that matches user inputs to similar contexts in an annotated dataset to explain attention-head learning.
Large language models can produce powerful contextual representations that lead to improvements across many NLP tasks. Since these models are typically guided by a sequence of learned self attention mechanisms and may comprise undesired inductive biases, it is paramount to be able to explore what the attention has learned. While static analyses of these models lead to targeted insights, interactive tools are more dynamic and can help humans better gain an intuition for the model-internal reasoning process. We present exBERT, an interactive tool named after the popular BERT language model, that provides insights into the meaning of the contextual representations by matching a human-specified input to similar contexts in a large annotated dataset. By aggregating the annotations of the matching similar contexts, exBERT helps intuitively explain what each attention-head has learned.