A Survey on Human-AI Teaming with Large Pre-Trained Models
Vanshika Vats Marzia Binta Nizam Minghao Liu Ziyuan Wang Richard Ho Mohnish Sai Prasad Vincent Titterton Sai Venkat Malreddy Riya Aggarwal Yanwen Xu Lei Ding Jay Mehta Nathan Grinnell Li Liu Sijia Zhong Devanathan Nallur Gandamani Xinyi Tang Rohan Ghosalkar Celeste Shen Rachel Shen Nafisa Hussain Kesav Ravichandran James Davis
In the rapidly evolving landscape of artificial intelligence (AI), the
collaboration between human intelligence and AI systems, known as Human-AI
(HAI) Teaming, has emerged as a cornerstone for advancing problem-solving and
decision-making processes. The advent of Large Pre-trained Models (LPtM) has
significantly transformed this landscape, offering unprecedented capabilities
by leveraging vast amounts of data to understand and predict complex patterns.
This paper surveys the pivotal integration of LPtMs with HAI, emphasizing how
these models enhance collaborative intelligence beyond traditional approaches.
It examines the synergistic potential of LPtMs in augmenting human
capabilities, discussing this collaboration for AI model improvements,
effective teaming, ethical considerations, and their broad applied implications
in various sectors. Through this exploration, the study sheds light on the
transformative impact of LPtM-enhanced HAI Teaming, providing insights for
future research, policy development, and strategic implementations aimed at
harnessing the full potential of this collaboration for research and societal
benefit.