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pro vyhledávání: '"Piet, Julien"'
Current LLMs are generally aligned to follow safety requirements and tend to refuse toxic prompts. However, LLMs can fail to refuse toxic prompts or be overcautious and refuse benign examples. In addition, state-of-the-art toxicity detectors have low
Externí odkaz:
http://arxiv.org/abs/2405.18822
Recent advances in Large Language Models (LLMs) enable exciting LLM-integrated applications, which perform text-based tasks by utilizing their advanced language understanding capabilities. However, as LLMs have improved, so have the attacks against t
Externí odkaz:
http://arxiv.org/abs/2402.06363
Autor:
Piet, Julien, Alrashed, Maha, Sitawarin, Chawin, Chen, Sizhe, Wei, Zeming, Sun, Elizabeth, Alomair, Basel, Wagner, David
Large Language Models (LLMs) are attracting significant research attention due to their instruction-following abilities, allowing users and developers to leverage LLMs for a variety of tasks. However, LLMs are vulnerable to prompt-injection attacks:
Externí odkaz:
http://arxiv.org/abs/2312.17673
The capabilities of large language models have grown significantly in recent years and so too have concerns about their misuse. It is important to be able to distinguish machine-generated text from human-authored content. Prior works have proposed nu
Externí odkaz:
http://arxiv.org/abs/2312.00273
Recent works have introduced input-convex neural networks (ICNNs) as learning models with advantageous training, inference, and generalization properties linked to their convex structure. In this paper, we propose a novel feature-convex neural networ
Externí odkaz:
http://arxiv.org/abs/2302.01961
Cryptocurrency miners have great latitude in deciding which transactions they accept, including their own, and the order in which they accept them. Ethereum miners in particular use this flexibility to collect MEV-Miner Extractable Value-by structuri
Externí odkaz:
http://arxiv.org/abs/2203.15930