Securing Reliability: A Brief Overview on Enhancing In-Context Learning
  for Foundation Models
Yunpeng Huang Yaonan Gu Jingwei Xu Zhihong Zhu Zhaorun Chen Xiaoxing Ma
As foundation models (FMs) continue to shape the landscape of AI, the
in-context learning (ICL) paradigm thrives but also encounters issues such as
toxicity, hallucination, disparity, adversarial vulnerability, and
inconsistency. Ensuring the reliability and responsibility of FMs is crucial
for the sustainable development of the AI ecosystem. In this concise overview,
we investigate recent advancements in enhancing the reliability and
trustworthiness of FMs within ICL frameworks, focusing on four key
methodologies, each with its corresponding subgoals. We sincerely hope this
paper can provide valuable insights for researchers and practitioners
endeavoring to build safe and dependable FMs and foster a stable and consistent
ICL environment, thereby unlocking their vast potential.