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of 17
pro vyhledávání: '"Wei, Boyi"'
Large language models are finetuned to refuse questions about hazardous knowledge, but these protections can often be bypassed. Unlearning methods aim at completely removing hazardous capabilities from models and make them inaccessible to adversaries
Externí odkaz:
http://arxiv.org/abs/2409.18025
Autor:
Wei, Boyi, Shi, Weijia, Huang, Yangsibo, Smith, Noah A., Zhang, Chiyuan, Zettlemoyer, Luke, Li, Kai, Henderson, Peter
Language models (LMs) derive their capabilities from extensive training on diverse data, including potentially copyrighted material. These models can memorize and generate content similar to their training data, posing potential concerns. Therefore,
Externí odkaz:
http://arxiv.org/abs/2406.18664
Autor:
Xie, Tinghao, Qi, Xiangyu, Zeng, Yi, Huang, Yangsibo, Sehwag, Udari Madhushani, Huang, Kaixuan, He, Luxi, Wei, Boyi, Li, Dacheng, Sheng, Ying, Jia, Ruoxi, Li, Bo, Li, Kai, Chen, Danqi, Henderson, Peter, Mittal, Prateek
Evaluating aligned large language models' (LLMs) ability to recognize and reject unsafe user requests is crucial for safe, policy-compliant deployments. Existing evaluation efforts, however, face three limitations that we address with SORRY-Bench, ou
Externí odkaz:
http://arxiv.org/abs/2406.14598
Event cameras are renowned for their high efficiency due to outputting a sparse, asynchronous stream of events. However, they are plagued by noisy events, especially in low light conditions. Denoising is an essential task for event cameras, but evalu
Externí odkaz:
http://arxiv.org/abs/2406.08909
Autor:
Qi, Xiangyu, Huang, Yangsibo, Zeng, Yi, Debenedetti, Edoardo, Geiping, Jonas, He, Luxi, Huang, Kaixuan, Madhushani, Udari, Sehwag, Vikash, Shi, Weijia, Wei, Boyi, Xie, Tinghao, Chen, Danqi, Chen, Pin-Yu, Ding, Jeffrey, Jia, Ruoxi, Ma, Jiaqi, Narayanan, Arvind, Su, Weijie J, Wang, Mengdi, Xiao, Chaowei, Li, Bo, Song, Dawn, Henderson, Peter, Mittal, Prateek
The exposure of security vulnerabilities in safety-aligned language models, e.g., susceptibility to adversarial attacks, has shed light on the intricate interplay between AI safety and AI security. Although the two disciplines now come together under
Externí odkaz:
http://arxiv.org/abs/2405.19524
Autor:
Wei, Boyi, Huang, Kaixuan, Huang, Yangsibo, Xie, Tinghao, Qi, Xiangyu, Xia, Mengzhou, Mittal, Prateek, Wang, Mengdi, Henderson, Peter
Large language models (LLMs) show inherent brittleness in their safety mechanisms, as evidenced by their susceptibility to jailbreaking and even non-malicious fine-tuning. This study explores this brittleness of safety alignment by leveraging pruning
Externí odkaz:
http://arxiv.org/abs/2402.05162
Video frame interpolation aims to generate high-quality intermediate frames from boundary frames and increase frame rate. While existing linear, symmetric and nonlinear models are used to bridge the gap from the lack of inter-frame motion, they canno
Externí odkaz:
http://arxiv.org/abs/2305.10198
Sampling is an important process in many GNN structures in order to train larger datasets with a smaller computational complexity. However, compared to other processes in GNN (such as aggregate, backward propagation), the sampling process still costs
Externí odkaz:
http://arxiv.org/abs/2209.02916
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