Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep
neural networks (DNNs) to execute complex inference tasks such as image classification …

Automatic short answer grading (ASAG) is the task of assessing short natural language
responses to objective questions using computational methods. The active research in this …

Large language models (LLMs) with hundreds of billions of parameters have sparked a new
wave of exciting AI applications. However, they are computationally expensive at inference …

Abstract Language models (LMs) are pretrained to imitate text from large and diverse
datasets that contain content that would violate human preferences if generated by an LM …

Fine-tuning language models (LMs) has yielded success on diverse downstream tasks, but
as LMs grow in size, backpropagation requires a prohibitively large amount of memory …

We propose Unified-IO, a model that performs a large variety of AI tasks spanning classical
computer vision tasks, including pose estimation, object detection, depth estimation and …

The conventional recipe for maximizing model accuracy is to (1) train multiple models with
various hyperparameters and (2) pick the individual model which performs best on a held …

Large language models (LMs) are able to in-context learn--perform a new task via inference
alone by conditioning on a few input-label pairs (demonstrations) and making predictions for …

Recently, ChatGPT has attracted great attention, as it can generate fluent and high-quality
responses to human inquiries. Several prior studies have shown that ChatGPT attains …

This paper introduces PowerInfer, a high-speed Large Language Model (LLM) inference
engine on a personal computer (PC) equipped with a single consumer-grade GPU. The key …