Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Xiong, Wayne"'
Autor:
Cheng, Yi, Liang, Xiao, Gong, Yeyun, Xiao, Wen, Wang, Song, Zhang, Yuji, Hou, Wenjun, Xu, Kaishuai, Liu, Wenge, Li, Wenjie, Jiao, Jian, Chen, Qi, Cheng, Peng, Xiong, Wayne
Self-consistency-based approaches, which involve repeatedly sampling multiple outputs and selecting the most consistent one as the final response, prove to be remarkably effective in improving the factual accuracy of large language models. Nonetheles
Externí odkaz:
http://arxiv.org/abs/2410.01556
Currently, prompting techniques can be mainly divided into two categories:1)shot method implicitly inspires the model to answer the question by mimicing the steps in the given example, e.g., the few-shot CoT. 2) Guideline method explicitly instructs
Externí odkaz:
http://arxiv.org/abs/2409.12979
Autor:
Wang, Song, Wang, Xun, Mei, Jie, Xie, Yujia, Muarray, Sean, Li, Zhang, Wu, Lingfeng, Chen, Si-Qing, Xiong, Wayne
Hallucination, a phenomenon where large language models (LLMs) produce output that is factually incorrect or unrelated to the input, is a major challenge for LLM applications that require accuracy and dependability. In this paper, we introduce a reli
Externí odkaz:
http://arxiv.org/abs/2407.15441
Autor:
Cai, Zefan, Zhang, Yichi, Gao, Bofei, Liu, Yuliang, Liu, Tianyu, Lu, Keming, Xiong, Wayne, Dong, Yue, Chang, Baobao, Hu, Junjie, Xiao, Wen
In this study, we investigate whether attention-based information flow inside large language models (LLMs) is aggregated through noticeable patterns for long context processing. Our observations reveal that LLMs aggregate information through Pyramida
Externí odkaz:
http://arxiv.org/abs/2406.02069
Summarizing lengthy documents is a common and essential task in our daily lives. Although recent advancements in neural summarization models can assist in crafting general-purpose summaries, human writers often have specific requirements that call fo
Externí odkaz:
http://arxiv.org/abs/2306.03067
Autor:
Zhang, Xingxing, Liu, Yiran, Wang, Xun, He, Pengcheng, Yu, Yang, Chen, Si-Qing, Xiong, Wayne, Wei, Furu
The input and output of most text generation tasks can be transformed to two sequences of tokens and they can be modeled using sequence-to-sequence learning modeling tools such as Transformers. These models are usually trained by maximizing the likel
Externí odkaz:
http://arxiv.org/abs/2212.04257
Autor:
He, Pengcheng, Peng, Baolin, Lu, Liyang, Wang, Song, Mei, Jie, Liu, Yang, Xu, Ruochen, Awadalla, Hany Hassan, Shi, Yu, Zhu, Chenguang, Xiong, Wayne, Zeng, Michael, Gao, Jianfeng, Huang, Xuedong
This paper presents Z-Code++, a new pre-trained language model optimized for abstractive text summarization. The model extends the state of the art encoder-decoder model using three techniques. First, we use a two-phase pre-training process to improv
Externí odkaz:
http://arxiv.org/abs/2208.09770
Autor:
Yoshioka, Takuya, Abramovski, Igor, Aksoylar, Cem, Chen, Zhuo, David, Moshe, Dimitriadis, Dimitrios, Gong, Yifan, Gurvich, Ilya, Huang, Xuedong, Huang, Yan, Hurvitz, Aviv, Jiang, Li, Koubi, Sharon, Krupka, Eyal, Leichter, Ido, Liu, Changliang, Parthasarathy, Partha, Vinnikov, Alon, Wu, Lingfeng, Xiao, Xiong, Xiong, Wayne, Wang, Huaming, Wang, Zhenghao, Zhang, Jun, Zhao, Yong, Zhou, Tianyan
This paper describes a system that generates speaker-annotated transcripts of meetings by using a microphone array and a 360-degree camera. The hallmark of the system is its ability to handle overlapped speech, which has been an unsolved problem in r
Externí odkaz:
http://arxiv.org/abs/1912.04979
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26 (2018) 184-196
Unsupervised single-channel overlapped speech recognition is one of the hardest problems in automatic speech recognition (ASR). Permutation invariant training (PIT) is a state of the art model-based approach, which applies a single neural network to
Externí odkaz:
http://arxiv.org/abs/1707.07048
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