A Survey on Human-AI Collaboration with Large Foundation Models
Abstract
As the capabilities of artificial intelligence (AI) continue to expand rapidly, Human-AI (HAI) Collaboration, combining human intellect and AI systems, has become pivotal for advancing problem-solving and decision-making processes. The advent of Large Foundation Models (LFMs) has greatly expanded its potential, offering unprecedented capabilities by leveraging vast amounts of data to understand and predict complex patterns. At the same time, realizing this potential responsibly requires addressing persistent challenges related to safety, fairness, and control. This paper reviews the crucial integration of LFMs with HAI, highlighting both opportunities and risks. We structure our analysis around four areas: human-guided model development, collaborative design principles, ethical and governance frameworks, and applications in high-stakes domains. Our review shows that successful HAI systems are not the automatic result of stronger models but the product of careful, human-centered design. By identifying key open challenges, this survey aims to give insight into current and future research that turns the raw power of LFMs into partnerships that are reliable, trustworthy, and beneficial to society.
Cite
@article{arxiv.2403.04931,
title = {A Survey on Human-AI Collaboration with Large Foundation Models},
author = {Vanshika Vats and Marzia Binta Nizam and Minghao Liu and Ziyuan Wang and Richard Ho and Mohnish Sai Prasad and Vincent Titterton and Sai Venkat Malreddy and Riya Aggarwal and Yanwen Xu and Lei Ding and Jay Mehta and Nathan Grinnell and Li Liu and Sijia Zhong and Devanathan Nallur Gandamani and Xinyi Tang and Rohan Ghosalkar and Celeste Shen and Rachel Shen and Nafisa Hussain and Kesav Ravichandran and James Davis},
journal= {arXiv preprint arXiv:2403.04931},
year = {2025}
}
Comments
Topic and scope refinement