Zobrazeno 1 - 10
of 599
pro vyhledávání: '"Yu Jiahao"'
Toxicity classification in textual content remains a significant problem. Data with labels from a single annotator fall short of capturing the diversity of human perspectives. Therefore, there is a growing need to incorporate crowdsourced annotations
Externí odkaz:
http://arxiv.org/abs/2410.14894
Fingerprinting large language models (LLMs) is essential for verifying model ownership, ensuring authenticity, and preventing misuse. Traditional fingerprinting methods often require significant computational overhead or white-box verification access
Externí odkaz:
http://arxiv.org/abs/2410.12318
We propose BlockFound, a customized foundation model for anomaly blockchain transaction detection. Unlike existing methods that rely on rule-based systems or directly apply off-the-shelf large language models, BlockFound introduces a series of custom
Externí odkaz:
http://arxiv.org/abs/2410.04039
Large Language Models (LLMs) have gained widespread use in various applications due to their powerful capability to generate human-like text. However, prompt injection attacks, which involve overwriting a model's original instructions with malicious
Externí odkaz:
http://arxiv.org/abs/2409.14729
Point clouds are commonly used in various practical applications such as autonomous driving and the manufacturing industry. However, these point clouds often suffer from incompleteness due to limited perspectives, scanner resolution and occlusion. Th
Externí odkaz:
http://arxiv.org/abs/2407.05008
Autor:
Luo, Haozheng, Yu, Jiahao, Zhang, Wenxin, Li, Jialong, Hu, Jerry Yao-Chieh, Xing, Xinyu, Liu, Han
We introduce a low-resource safety enhancement method for aligning large language models (LLMs) without the need for supervised fine-tuning (SFT) or reinforcement learning from human feedback (RLHF). Our main idea is to exploit knowledge distillation
Externí odkaz:
http://arxiv.org/abs/2406.01514
Along with the remarkable successes of Language language models, recent research also started to explore the security threats of LLMs, including jailbreaking attacks. Attackers carefully craft jailbreaking prompts such that a target LLM will respond
Externí odkaz:
http://arxiv.org/abs/2405.20653
Deep reinforcement learning (DRL) is playing an increasingly important role in real-world applications. However, obtaining an optimally performing DRL agent for complex tasks, especially with sparse rewards, remains a significant challenge. The train
Externí odkaz:
http://arxiv.org/abs/2405.03064
Autor:
Yu, Jiahao, Chen, Li
Recent medical image segmentation methods apply implicit neural representation (INR) to the decoder for achieving a continuous coordinate decoding to tackle the drawback of conventional discrete grid-based data representations. However, the INR-based
Externí odkaz:
http://arxiv.org/abs/2404.09472
Autor:
Yu, Jiahao, Duan, Yihai, Xu, Longfei, Chen, Chao, Liu, Shuliang, Liu, Kaikui, Yang, Fan, Chu, Xiangxiang, Guo, Ning
Multi-scenario route ranking (MSRR) is crucial in many industrial mapping systems. However, the industrial community mainly adopts interactive interfaces to encourage users to select pre-defined scenarios, which may hinder the downstream ranking perf
Externí odkaz:
http://arxiv.org/abs/2404.00243