Zobrazeno 1 - 10
of 173
pro vyhledávání: '"Tang, Tianyi"'
Autor:
Tang, Tianyi, Hu, Yiwen, Li, Bingqian, Luo, Wenyang, Qin, Zijing, Sun, Haoxiang, Wang, Jiapeng, Xu, Shiyi, Cheng, Xiaoxue, Guo, Geyang, Peng, Han, Zheng, Bowen, Tang, Yiru, Min, Yingqian, Chen, Yushuo, Chen, Jie, Zhao, Yuanqian, Ding, Luran, Wang, Yuhao, Dong, Zican, Xia, Chunxuan, Li, Junyi, Zhou, Kun, Zhao, Wayne Xin, Wen, Ji-Rong
To facilitate the research on large language models (LLMs), this paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of LLMs. This library is featured with three main merits: (1) a unified data int
Externí odkaz:
http://arxiv.org/abs/2407.05563
Autor:
Chen, Yushuo, Tang, Tianyi, Xiang, Erge, Li, Linjiang, Zhao, Wayne Xin, Wang, Jing, Chai, Yunpeng, Wen, Ji-Rong
In real world, large language models (LLMs) can serve as the assistant to help users accomplish their jobs, and also support the development of advanced applications. For the wide application of LLMs, the inference efficiency is an essential concern,
Externí odkaz:
http://arxiv.org/abs/2404.11502
Autor:
Tang, Tianyi, Luo, Wenyang, Huang, Haoyang, Zhang, Dongdong, Wang, Xiaolei, Zhao, Xin, Wei, Furu, Wen, Ji-Rong
Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora. It remains a challenging problem to explain the underlying mechanisms by which LLMs process mu
Externí odkaz:
http://arxiv.org/abs/2402.16438
The direct growth of III-V semiconductors on silicon holds tremendous potential for photonics applications. However, the inherent differences in their properties lead to defects in the epitaxial layer, including threading dislocations (TDs), antiphas
Externí odkaz:
http://arxiv.org/abs/2312.15390
Alignment with human preference is a desired property of large language models (LLMs). Currently, the main alignment approach is based on reinforcement learning from human feedback (RLHF). Despite the effectiveness of RLHF, it is intricate to impleme
Externí odkaz:
http://arxiv.org/abs/2311.04072
Large language models (LLMs) have achieved dramatic proficiency over NLP tasks with normal length. Recently, multiple studies have committed to extending the context length and enhancing the long text modeling capabilities of LLMs. To comprehensively
Externí odkaz:
http://arxiv.org/abs/2309.13345
Interpreting ancient Chinese has been the key to comprehending vast Chinese literature, tradition, and civilization. In this paper, we propose Erya for ancient Chinese translation. From a dataset perspective, we collect, clean, and classify ancient C
Externí odkaz:
http://arxiv.org/abs/2308.00240
In this paper, we propose a novel language model guided captioning approach, LAMOC, for knowledge-based visual question answering (VQA). Our approach employs the generated captions by a captioning model as the context of an answer prediction model, w
Externí odkaz:
http://arxiv.org/abs/2305.17006
People often imagine relevant scenes to aid in the writing process. In this work, we aim to utilize visual information for composition in the same manner as humans. We propose a method, LIVE, that makes pre-trained language models (PLMs) Learn to Ima
Externí odkaz:
http://arxiv.org/abs/2305.16944
Autor:
Tang, Tianyi, Lu, Hongyuan, Jiang, Yuchen Eleanor, Huang, Haoyang, Zhang, Dongdong, Zhao, Wayne Xin, Kocmi, Tom, Wei, Furu
Most research about natural language generation (NLG) relies on evaluation benchmarks with limited references for a sample, which may result in poor correlations with human judgements. The underlying reason is that one semantic meaning can actually b
Externí odkaz:
http://arxiv.org/abs/2305.15067