On Catastrophic Inheritance of Large Foundation Models
Hao Chen Bhiksha Raj Xing Xie Jindong Wang
Large foundation models (LFMs) are claiming incredible performances. Yet
great concerns have been raised about their mythic and uninterpreted potentials
not only in machine learning, but also in various other disciplines. In this
position paper, we propose to identify a neglected issue deeply rooted in LFMs:
Catastrophic Inheritance, describing the weaknesses and limitations inherited
from biased large-scale pre-training data to behaviors of LFMs on the
downstream tasks, including samples that are corrupted, long-tailed, noisy,
out-of-distributed, to name a few. Such inheritance can potentially cause
catastrophes to downstream applications, such as bias, lack of generalization,
deteriorated performance, security vulnerability, privacy leakage, and value
misalignment. We discuss the challenges behind this issue and propose UIM, a
framework to Understand the catastrophic inheritance of LFMs from both
pre-training and downstream adaptation, Interpret the implications of
catastrophic inheritance on downstream tasks, and how to Mitigate it. UIM aims
to unite both the machine learning and social sciences communities for more
responsible and promising AI development and deployment.