Search Results for author:
Found 7 papers, 3 papers with code
Using Captum to Explain Generative Language Models
Captum is a comprehensive library for model explainability in PyTorch, offering a range of methods from the interpretability literature to enhance users' understanding of PyTorch models.
Error Discovery by Clustering Influence Embeddings
We present a method for identifying groups of test examples -- slices -- on which a model under-performs, a task now known as slice discovery.
Intermediate Entity-based Sparse Interpretable Representation Learning
However, while fine-tuning sparse, interpretable representations improves accuracy on downstream tasks, it destroys the semantics of the dimensions which were enforced in pre-training.
Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection
In this work, we empirically study how and whether such methods, applied in a bi-modal setting, can improve an existing VQA system's performance on the KBVQA task.
Biomedical Interpretable Entity Representations
Pre-trained language models induce dense entity representations that offer strong performance on entity-centric NLP tasks, but such representations are not immediately interpretable.
Learning Dense Representations for Entity Retrieval
We show that it is feasible to perform entity linking by training a dual encoder (two-tower) model that encodes mentions and entities in the same dense vector space, where candidate entities are retrieved by approximate nearest neighbor search.
Explaining Deep Classification of Time-Series Data with Learned Prototypes
We improve upon existing models by optimizing for increased prototype diversity and robustness, visualize how these prototypes in the latent space are used by the model to distinguish classes, and show that prototypes are capable of learning features on two dimensional time-series data to produce explainable insights during classification tasks.
