mlsanityData to 13 Oct 2026

Securing Reliability: A Brief Overview on Enhancing In-Context Learning for Foundation Models

arXiv 2402.17671 ↗First posted 27 Feb 2024cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 60 of its 92 references that are in the map. First posted on arXiv 27 Feb 2024.

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60 of 92 references.

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