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Securing Reliability: A Brief Overview on Enhancing In-Context Learning for Foundation Models
Feb. 28, 2024, 5:42 a.m. | Yunpeng Huang, Yaonan Gu, Jingwei Xu, Zhihong Zhu, Zhaorun Chen, Xiaoxing Ma
cs.LG updates on arXiv.org arxiv.org
Abstract: 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 …
abstract adversarial arxiv context cs.lg development foundation hallucination in-context learning landscape overview paradigm reliability responsibility sustainable sustainable development toxicity type vulnerability
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