Zobrazeno 1 - 10
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pro vyhledávání: '"Jiang, Huiqiang"'
Autor:
Jiang, Huiqiang, Li, Yucheng, Zhang, Chengruidong, Wu, Qianhui, Luo, Xufang, Ahn, Surin, Han, Zhenhua, Abdi, Amir H., Li, Dongsheng, Lin, Chin-Yew, Yang, Yuqing, Qiu, Lili
The computational challenges of Large Language Model (LLM) inference remain a significant barrier to their widespread deployment, especially as prompt lengths continue to increase. Due to the quadratic complexity of the attention computation, it take
Externí odkaz:
http://arxiv.org/abs/2407.02490
Autor:
Yu, Yijiong, Jiang, Huiqiang, Luo, Xufang, Wu, Qianhui, Lin, Chin-Yew, Li, Dongsheng, Yang, Yuqing, Huang, Yongfeng, Qiu, Lili
Large Language Models (LLMs) are increasingly applied in various real-world scenarios due to their excellent generalization capabilities and robust generative abilities. However, they exhibit position bias, also known as "lost in the middle", a pheno
Externí odkaz:
http://arxiv.org/abs/2406.02536
The performance of large language models (LLMs) is significantly influenced by the quality of the prompts provided. In response, researchers have developed enormous prompt engineering strategies aimed at modifying the prompt text to enhance task perf
Externí odkaz:
http://arxiv.org/abs/2404.11216
Autor:
Pan, Zhuoshi, Wu, Qianhui, Jiang, Huiqiang, Xia, Menglin, Luo, Xufang, Zhang, Jue, Lin, Qingwei, Rühle, Victor, Yang, Yuqing, Lin, Chin-Yew, Zhao, H. Vicky, Qiu, Lili, Zhang, Dongmei
This paper focuses on task-agnostic prompt compression for better generalizability and efficiency. Considering the redundancy in natural language, existing approaches compress prompts by removing tokens or lexical units according to their information
Externí odkaz:
http://arxiv.org/abs/2403.12968
Autor:
Jiang, Huiqiang, Wu, Qianhui, Luo, Xufang, Li, Dongsheng, Lin, Chin-Yew, Yang, Yuqing, Qiu, Lili
In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in
Externí odkaz:
http://arxiv.org/abs/2310.06839
Large language models (LLMs) have been applied in various applications due to their astonishing capabilities. With advancements in technologies such as chain-of-thought (CoT) prompting and in-context learning (ICL), the prompts fed to LLMs are becomi
Externí odkaz:
http://arxiv.org/abs/2310.05736
Autor:
Liang, Yukang, Song, Kaitao, Mao, Shaoguang, Jiang, Huiqiang, Qiu, Luna, Yang, Yuqing, Li, Dongsheng, Xu, Linli, Qiu, Lili
Pronunciation assessment is a major challenge in the computer-aided pronunciation training system, especially at the word (phoneme)-level. To obtain word (phoneme)-level scores, current methods usually rely on aligning components to obtain acoustic f
Externí odkaz:
http://arxiv.org/abs/2306.02682
Autor:
Jiang, Huiqiang, Zhang, Li Lyna, Li, Yuang, Wu, Yu, Cao, Shijie, Cao, Ting, Yang, Yuqing, Li, Jinyu, Yang, Mao, Qiu, Lili
Automatic Speech Recognition (ASR) has seen remarkable advancements with deep neural networks, such as Transformer and Conformer. However, these models typically have large model sizes and high inference costs, posing a challenge to deploy on resourc
Externí odkaz:
http://arxiv.org/abs/2305.19549
Cross-lingual named entity recognition (NER) aims to train an NER system that generalizes well to a target language by leveraging labeled data in a given source language. Previous work alleviates the data scarcity problem by translating source-langua
Externí odkaz:
http://arxiv.org/abs/2305.14913
Autor:
Tang, Chen, Zhang, Li Lyna, Jiang, Huiqiang, Xu, Jiahang, Cao, Ting, Zhang, Quanlu, Yang, Yuqing, Wang, Zhi, Yang, Mao
Neural Architecture Search (NAS) has shown promising performance in the automatic design of vision transformers (ViT) exceeding 1G FLOPs. However, designing lightweight and low-latency ViT models for diverse mobile devices remains a big challenge. In
Externí odkaz:
http://arxiv.org/abs/2303.09730