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Found 5 papers, 4 papers with code
Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation
In response, we introduce Pentathlon, a benchmark for holistic and realistic evaluation of model efficiency.
The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment
In this work, we examine this phenomenon through a series of case studies analyzing the effects of model design decisions, framework paradigms, and hardware platforms on total model latency.
CIGLI: Conditional Image Generation from Language & Image
Instead of generating an image based on text as in text-image generation, this task requires the generation of an image from a textual description and an image prompt.
Generative Data Augmentation for Commonsense Reasoning
Recent advances in commonsense reasoning depend on large-scale human-annotated training data to achieve peak performance.
Ranked #1 on
Question Answering
on CODAH
CODAH: An Adversarially Authored Question-Answer Dataset for Common Sense
To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems.
Ranked #1 on
Common Sense Reasoning
on CODAH
(using extra training data)
