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.

翻译：随着基础模型持续塑造人工智能的发展格局，上下文学习范式虽蓬勃发展，却也面临着毒性、幻觉、偏差、对抗脆弱性及不一致性等问题。确保基础模型的可靠性与责任性是人工智能生态系统可持续发展的关键。本综述简要探讨了在上下文学习框架内提升基础模型可靠性与可信度的最新进展，聚焦于四种关键方法论及其对应的子目标。我们衷心期望本文能为致力于构建安全可靠的基础模型、营造稳定一致的上下文学习环境，从而释放其巨大潜力的研究人员和从业者提供有价值的见解。