Specifically, we extract relevant commonsense knowledge in inputs as references to align model behavior with human knowledge.
HAPI is the first large-scale dataset of ML API usages and is a unique resource for studying ML-as-a-service (MLaaS).
Since shortcuts vary in coverage, productivity, and semantic meaning, it is challenging for NLU experts to systematically understand and avoid them when creating benchmark datasets.
However, those models do not consider the numerical properties of numbers and cannot perform robustly on numerical reasoning tasks (e. g., math word problems and measurement estimation).
Much research focuses on modeling the complex intra- and inter-modal interactions between different communication channels.
Two case studies and interviews with domain experts demonstrate the effectiveness of GNNLens in facilitating the understanding of GNN models and their errors.
To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts.