Zobrazeno 1 - 10
of 188
pro vyhledávání: '"Liu Sizhe"'
Decoding by contrasting layers (DoLa), is designed to improve the generation quality of large language models (LLMs) by contrasting the prediction probabilities between an early exit output (amateur logits) and the final output (expert logits). Howev
Externí odkaz:
http://arxiv.org/abs/2407.10795
Autor:
Hu, Peng, Liu, Sizhe, Gao, Changjiang, Huang, Xin, Han, Xue, Feng, Junlan, Deng, Chao, Huang, Shujian
Large Language Models have demonstrated impressive reasoning capabilities across multiple languages. However, the relationship between capabilities in different languages is less explored. In this work, we decompose the process of reasoning tasks int
Externí odkaz:
http://arxiv.org/abs/2406.16655
Autor:
Zhou, Jingbo, Chen, Shaorong, Xia, Jun, Liu, Sizhe, Ling, Tianze, Du, Wenjie, Liu, Yue, Yin, Jianwei, Li, Stan Z.
Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the high-throughput analysis of protein composition in biological tissues. Many deep learning methods have been developed for \emph{de novo} peptide sequencing task,
Externí odkaz:
http://arxiv.org/abs/2406.11906
Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the analysis of protein composition in biological samples. Despite the development of various deep learning methods for identifying amino acid sequences (peptides) r
Externí odkaz:
http://arxiv.org/abs/2403.07013
Autor:
Zhu, Wenhao, Zhao, Qianfeng, Lv, Yunzhe, Huang, Shujian, Zhao, Siheng, Liu, Sizhe, Chen, Jiajun
Augmenting the base neural model with a token-level symbolic datastore is a novel generation paradigm and has achieved promising results in machine translation (MT). In this paper, we introduce a unified framework kNN-BOX, which enables quick develop
Externí odkaz:
http://arxiv.org/abs/2302.13574
Autor:
Chen, Tan, Huang, Zhe, Motes, James, Geng, Junyi, Ta, Quang Minh, Dinkel, Holly, Abdul-Rashid, Hameed, Myers, Jessica, Mun, Ye-Ji, Lin, Wei-che, Huang, Yuan-yung, Liu, Sizhe, Morales, Marco, Amato, Nancy M., Driggs-Campbell, Katherine, Bretl, Timothy
Significant progress in robotics reveals new opportunities to advance manufacturing. Next-generation industrial automation will require both integration of distinct robotic technologies and their application to challenging industrial environments. Th
Externí odkaz:
http://arxiv.org/abs/2205.14340
Publikováno v:
In Knowledge-Based Systems 3 August 2024 297
Publikováno v:
In Information Sciences January 2025 687
Publikováno v:
SAGE Open. Jul-Sep2024, Vol. 14 Issue 3, p1-13. 13p.
Autor:
Liu, Sizhe, Watanabe, Kenta, Miwa, Eiji, Hara, Mitsuo, Seki, Takahiro, Mayumi, Koichi, Ito, Kohzo, Takeoka, Yukikazu
Publikováno v:
In Giant March 2024 17