Zobrazeno 1 - 10
of 87 035
pro vyhledávání: '"P. Guard"'
Collaborative Perception (CP) has shown a promising technique for autonomous driving, where multiple connected and autonomous vehicles (CAVs) share their perception information to enhance the overall perception performance and expand the perception r
Externí odkaz:
http://arxiv.org/abs/2412.12000
Autor:
Fedorov, Igor, Plawiak, Kate, Wu, Lemeng, Elgamal, Tarek, Suda, Naveen, Smith, Eric, Zhan, Hongyuan, Chi, Jianfeng, Hulovatyy, Yuriy, Patel, Kimish, Liu, Zechun, Zhao, Changsheng, Shi, Yangyang, Blankevoort, Tijmen, Pasupuleti, Mahesh, Soran, Bilge, Coudert, Zacharie Delpierre, Alao, Rachad, Krishnamoorthi, Raghuraman, Chandra, Vikas
This paper presents Llama Guard 3-1B-INT4, a compact and efficient Llama Guard model, which has been open-sourced to the community during Meta Connect 2024. We demonstrate that Llama Guard 3-1B-INT4 can be deployed on resource-constrained devices, ac
Externí odkaz:
http://arxiv.org/abs/2411.17713
Autor:
Chi, Jianfeng, Karn, Ujjwal, Zhan, Hongyuan, Smith, Eric, Rando, Javier, Zhang, Yiming, Plawiak, Kate, Coudert, Zacharie Delpierre, Upasani, Kartikeya, Pasupuleti, Mahesh
We introduce Llama Guard 3 Vision, a multimodal LLM-based safeguard for human-AI conversations that involves image understanding: it can be used to safeguard content for both multimodal LLM inputs (prompt classification) and outputs (response classif
Externí odkaz:
http://arxiv.org/abs/2411.10414
Autor:
Wang, Minjia, Lin, Pingping, Cai, Siqi, An, Shengnan, Ma, Shengjie, Lin, Zeqi, Huang, Congrui, Xu, Bixiong
Content moderation, the process of reviewing and monitoring the safety of generated content, is important for development of welcoming online platforms and responsible large language models. Content moderation contains various tasks, each with its un
Externí odkaz:
http://arxiv.org/abs/2411.05214
Autor:
Shaiek, Oumayma, Yin, Huifei, Uesako, Nodoka, Islam, Md Moshiul, Rhaman, Mohammad Saidur, Nakamura, Toshiyuki, Nakamura, Yoshimasa, Munemasa, Shintaro, Mano, Jun'ichi, Murata, Yoshiyuki
Publikováno v:
Bioscience, Biotechnology & Biochemistry; Dec2024, Vol. 88 Issue 12, p1403-1410, 8p
Large language models (LLMs) pose significant risks due to the potential for generating harmful content or users attempting to evade guardrails. Existing studies have developed LLM-based guard models designed to moderate the input and output of threa
Externí odkaz:
http://arxiv.org/abs/2410.10414
Autor:
Lee, Seanie, Seong, Haebin, Lee, Dong Bok, Kang, Minki, Chen, Xiaoyin, Wagner, Dominik, Bengio, Yoshua, Lee, Juho, Hwang, Sung Ju
Safety guard models that detect malicious queries aimed at large language models (LLMs) are essential for ensuring the secure and responsible deployment of LLMs in real-world applications. However, deploying existing safety guard models with billions
Externí odkaz:
http://arxiv.org/abs/2410.01524
In this paper, we present Safe Guard, an LLM-agent for the detection of hate speech in voice-based interactions in social VR (VRChat). Our system leverages Open AI GPT and audio feature extraction for real-time voice interactions. We contribute a sys
Externí odkaz:
http://arxiv.org/abs/2409.15623
The misuse of deep learning-based facial manipulation poses a potential threat to civil rights. To prevent this fraud at its source, proactive defense technology was proposed to disrupt the manipulation process by adding invisible adversarial perturb
Externí odkaz:
http://arxiv.org/abs/2409.13349
Recent advances in diffusion models have significantly enhanced their ability to generate high-quality images and videos, but they have also increased the risk of producing unsafe content. Existing unlearning/editing-based methods for safe generation
Externí odkaz:
http://arxiv.org/abs/2410.12761