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Found 8 papers, 4 papers with code
Holistically Evaluating the Environmental Impact of Creating Language Models
As the performance of artificial intelligence systems has dramatically increased, so too has the environmental impact of creating these systems.
Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training
Dramatic increases in the capabilities of neural network models in recent years are driven by scaling model size, training data, and corresponding computational resources.
Gradient Localization Improves Lifelong Pretraining of Language Models
Large Language Models (LLMs) trained on web-scale text corpora have been shown to capture world knowledge in their parameters.
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)
