Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Tang, Zecheng"'
Long-context models(LCMs) have shown great potential in processing long input sequences(even more than 100M tokens) conveniently and effectively. With significant progress, recent research has pointed out that LCMs can accurately locate token-level s
Externí odkaz:
http://arxiv.org/abs/2410.18533
Large language models (LLMs) have advanced significantly due to the attention mechanism, but their quadratic complexity and linear memory demands limit their performance on long-context tasks. Recently, researchers introduced Mamba, an advanced model
Externí odkaz:
http://arxiv.org/abs/2410.15678
The rapid development of generative AI is a double-edged sword, which not only facilitates content creation but also makes image manipulation easier and more difficult to detect. Although current image forgery detection and localization (IFDL) method
Externí odkaz:
http://arxiv.org/abs/2410.02761
Long-context models (LCMs) have made remarkable strides in recent years, offering users great convenience for handling tasks that involve long context, such as document summarization. As the community increasingly prioritizes the faithfulness of gene
Externí odkaz:
http://arxiv.org/abs/2410.02115
Recent advancements in Large Language Models (LLMs) have yielded remarkable success across diverse fields. However, handling long contexts remains a significant challenge for LLMs due to the quadratic time and space complexity of attention mechanisms
Externí odkaz:
http://arxiv.org/abs/2408.16967
Autor:
Qiao, Dan, Su, Yi, Wang, Pinzheng, Ye, Jing, Xie, Wenjing, Zhou, Yuechi, Ding, Yuyang, Tang, Zecheng, Wang, Jikai, Ji, Yixin, Wang, Yue, Guo, Pei, Sun, Zechen, Zhang, Zikang, Li, Juntao, Chao, Pingfu, Chen, Wenliang, Fu, Guohong, Zhou, Guodong, Zhu, Qiaoming, Zhang, Min
Large Language Models (LLMs) have played an important role in many fields due to their powerful capabilities.However, their massive number of parameters leads to high deployment requirements and incurs significant inference costs, which impedes their
Externí odkaz:
http://arxiv.org/abs/2405.05957
Large Language Models (LLMs) have demonstrated impressive capabilities for generalizing in unseen tasks. In the Named Entity Recognition (NER) task, recent advancements have seen the remarkable improvement of LLMs in a broad range of entity domains v
Externí odkaz:
http://arxiv.org/abs/2402.16602
Autor:
Tang, Zecheng, Wu, Chenfei, Zhang, Zekai, Ni, Mingheng, Yin, Shengming, Liu, Yu, Yang, Zhengyuan, Wang, Lijuan, Liu, Zicheng, Li, Juntao, Duan, Nan
To leverage LLMs for visual synthesis, traditional methods convert raster image information into discrete grid tokens through specialized visual modules, while disrupting the model's ability to capture the true semantic representation of visual scene
Externí odkaz:
http://arxiv.org/abs/2401.17093
Recent studies have revealed that grammatical error correction methods in the sequence-to-sequence paradigm are vulnerable to adversarial attack, and simply utilizing adversarial examples in the pre-training or post-training process can significantly
Externí odkaz:
http://arxiv.org/abs/2310.13321
Autor:
Li, Juntao, Tang, Zecheng, Ding, Yuyang, Wang, Pinzheng, Guo, Pei, You, Wangjie, Qiao, Dan, Chen, Wenliang, Fu, Guohong, Zhu, Qiaoming, Zhou, Guodong, Zhang, Min
Large language models (LLMs) with billions of parameters have demonstrated outstanding performance on various natural language processing tasks. This report presents OpenBA, an open-sourced 15B bilingual asymmetric seq2seq model, to contribute an LLM
Externí odkaz:
http://arxiv.org/abs/2309.10706