Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Feng, Zhangyin"'
Autor:
Feng, Zhangyin, Hu, Runyi, Liu, Liangxin, Zhang, Fan, Tang, Duyu, Dai, Yong, Feng, Xiaocheng, Li, Jiwei, Qin, Bing, Shi, Shuming
Autoregressive and diffusion models drive the recent breakthroughs on text-to-image generation. Despite their huge success of generating high-realistic images, a common shortcoming of these models is their high inference latency - autoregressive mode
Externí odkaz:
http://arxiv.org/abs/2312.14988
Autor:
Feng, Zhangyin, Ma, Weitao, Yu, Weijiang, Huang, Lei, Wang, Haotian, Chen, Qianglong, Peng, Weihua, Feng, Xiaocheng, Qin, Bing, liu, Ting
Large language models (LLMs) exhibit superior performance on various natural language tasks, but they are susceptible to issues stemming from outdated data and domain-specific limitations. In order to address these challenges, researchers have pursue
Externí odkaz:
http://arxiv.org/abs/2311.05876
Autor:
Huang, Lei, Yu, Weijiang, Ma, Weitao, Zhong, Weihong, Feng, Zhangyin, Wang, Haotian, Chen, Qianglong, Peng, Weihua, Feng, Xiaocheng, Qin, Bing, Liu, Ting
The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), leading to remarkable advancements in text understanding and generation. Nevertheless, alongside these strides, LLMs exhibit a c
Externí odkaz:
http://arxiv.org/abs/2311.05232
Large language models augmented with task-relevant documents have demonstrated impressive performance on knowledge-intensive tasks. However, regarding how to obtain effective documents, the existing methods are mainly divided into two categories. One
Externí odkaz:
http://arxiv.org/abs/2310.05149
Autor:
Feng, Zhangyin, Dai, Yong, Zhang, Fan, Tang, Duyu, Feng, Xiaocheng, Wu, Shuangzhi, Qin, Bing, Cao, Yunbo, Shi, Shuming
Traditional multitask learning methods basically can only exploit common knowledge in task- or language-wise, which lose either cross-language or cross-task knowledge. This paper proposes a general multilingual multitask model, named SkillNet-X, whic
Externí odkaz:
http://arxiv.org/abs/2306.16176
Autor:
Feng, Zhangyin, Ren, Yuchen, Yu, Xinmiao, Feng, Xiaocheng, Tang, Duyu, Shi, Shuming, Qin, Bing
Diffusion models developed on top of powerful text-to-image generation models like Stable Diffusion achieve remarkable success in visual story generation. However, the best-performing approach considers historically generated results as flattened mem
Externí odkaz:
http://arxiv.org/abs/2305.16811
One Model, Multiple Modalities: A Sparsely Activated Approach for Text, Sound, Image, Video and Code
Autor:
Dai, Yong, Tang, Duyu, Liu, Liangxin, Tan, Minghuan, Zhou, Cong, Wang, Jingquan, Feng, Zhangyin, Zhang, Fan, Hu, Xueyu, Shi, Shuming
People perceive the world with multiple senses (e.g., through hearing sounds, reading words and seeing objects). However, most existing AI systems only process an individual modality. This paper presents an approach that excels at handling multiple m
Externí odkaz:
http://arxiv.org/abs/2205.06126
Chinese BERT models achieve remarkable progress in dealing with grammatical errors of word substitution. However, they fail to handle word insertion and deletion because BERT assumes the existence of a word at each position. To address this, we prese
Externí odkaz:
http://arxiv.org/abs/2204.12052
Autor:
Dai, Yong, Li, Linyang, Zhou, Cong, Feng, Zhangyin, Zhao, Enbo, Qiu, Xipeng, Li, Piji, Tang, Duyu
Whole word masking (WWM), which masks all subwords corresponding to a word at once, makes a better English BERT model. For the Chinese language, however, there is no subword because each token is an atomic character. The meaning of a word in Chinese
Externí odkaz:
http://arxiv.org/abs/2203.00286
Autor:
Tan, Minghuan, Dai, Yong, Tang, Duyu, Feng, Zhangyin, Huang, Guoping, Jiang, Jing, Li, Jiwei, Shi, Shuming
While GPT has become the de-facto method for text generation tasks, its application to pinyin input method remains unexplored. In this work, we make the first exploration to leverage Chinese GPT for pinyin input method. We find that a frozen GPT achi
Externí odkaz:
http://arxiv.org/abs/2203.00249