Demonstrations Are All You Need: Advancing Offensive Content
Paraphrasing using In-Context Learning

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Demonstrations Are All You Need: Advancing Offensive Content Paraphrasing using In-Context Learning

Annual Meeting of the Association for Computational Linguistics (ACL), 2023

16 October 2023

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Papers citing "Demonstrations Are All You Need: Advancing Offensive Content Paraphrasing using In-Context Learning"

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Title
Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM Chi Zhang Changjia Zhu Junjie Xiong Xiaoran Xu Jinkui Chi Yao Liu Zhuo Lu ELM 187 3 0 07 Aug 2025
What's Taboo for You? - An Empirical Evaluation of LLMs Behavior Toward Sensitive Content Alfio Ferrara Sergio Picascia Laura Pinnavaia Vojimir Ranitovic Elisabetta Rocchetti Alice Tuveri 74 0 0 31 Jul 2025
Can large language models explore in-context?Neural Information Processing Systems (NeurIPS), 2024 Akshay Krishnamurthy Keegan Harris Dylan J. Foster Cyril Zhang Aleksandrs Slivkins LM&Ro LLMAG LRM 554 52 0 22 Mar 2024
In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space SteeringInternational Conference on Machine Learning (ICML), 2023 Sheng Liu Haotian Ye Lei Xing James Y. Zou 234 198 0 11 Nov 2023