exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformer Models
Large Transformer-based language models can route and reshape complex information via their multi-headed attention mechanism. Although the attention never receives explicit supervision, it can exhibit recognizable patterns following linguistic or positional information. Analyzing the learned representations and attentions is paramount to furthering our understanding of the inner workings of these models. However, analyses have to catch up with the rapid release of new models and the growing diversity of investigation techniques. To support analysis for a wide variety of models, we introduce exBERT, a tool to help humans conduct flexible, interactive investigations and formulate hypotheses for the model-internal reasoning process. exBERT provides insights into the meaning of the contextual representations and attention by matching a human-specified input to similar contexts in large annotated datasets. By aggregating the annotations of the matched contexts, exBERT can quickly replicate findings from literature and extend them to previously not analyzed models.
Code (1)
Tasks
DiversitySimilar Papers 제목 키워드 기반
exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models
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 co…
Language ModelingLanguage ModellingEffect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation
Although deep learning models for chest X-ray interpretation are commonly trained on labels generated by automatic radiology report labelers, the impact of improvements in report labeling on the performance of chest X-ra…
ClassificationGeneral Classificationimage-classificationImage Classification+1VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels
Automatic extraction of medical conditions from free-text radiology reports is critical for supervising computer vision models to interpret medical images. In this work, we show that radiologists labeling reports signifi…
exBERT: Extending Pre-trained Models with Domain-specific Vocabulary Under Constrained Training Resources
We introduce exBERT, a training method to extend BERT pre-trained models from a general domain to a new pre-trained model for a specific domain with a new additive vocabulary under constrained training resources (i.e., c…
ArticlesMIMOQA: Multimodal Input Multimodal Output Question Answering
Multimodal research has picked up significantly in the space of question answering with the task being extended to visual question answering, charts question answering as well as multimodal input question answering. Howe…
Question AnsweringVisual Question AnsweringVisual Question Answering (VQA)