A Survey on Human-AI Collaboration with Large Foundation Models
It addresses the challenge of responsibly leveraging LFMs in HAI for researchers and practitioners, focusing on safety, fairness, and control, but is incremental as it reviews existing work.
This survey examines the integration of Large Foundation Models (LFMs) with Human-AI Collaboration (HAI) to enhance problem-solving and decision-making, highlighting that successful systems require careful human-centered design rather than just stronger models.
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.