Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Zhang, Ruohong"'
Autor:
Zhang, Ruohong, Gui, Liangke, Sun, Zhiqing, Feng, Yihao, Xu, Keyang, Zhang, Yuanhan, Fu, Di, Li, Chunyuan, Hauptmann, Alexander, Bisk, Yonatan, Yang, Yiming
Preference modeling techniques, such as direct preference optimization (DPO), has shown effective in enhancing the generalization abilities of large language model (LLM). However, in tasks involving video instruction-following, providing informative
Externí odkaz:
http://arxiv.org/abs/2404.01258
Autor:
Zhang, Ruohong, Gao, Luyu, Zheng, Chen, Fan, Zhen, Lai, Guokun, Zhang, Zheng, Ai, Fangzhou, Yang, Yiming, Yang, Hongxia
Large Language Models (LLMs), despite their great power in language generation, often encounter challenges when dealing with intricate and knowledge-demanding queries in specific domains. This paper introduces a novel approach to enhance LLMs by effe
Externí odkaz:
http://arxiv.org/abs/2311.10614
Autor:
Zhou, Xuhui, Zhu, Hao, Mathur, Leena, Zhang, Ruohong, Yu, Haofei, Qi, Zhengyang, Morency, Louis-Philippe, Bisk, Yonatan, Fried, Daniel, Neubig, Graham, Sap, Maarten
Humans are social beings; we pursue social goals in our daily interactions, which is a crucial aspect of social intelligence. Yet, AI systems' abilities in this realm remain elusive. We present SOTOPIA, an open-ended environment to simulate complex s
Externí odkaz:
http://arxiv.org/abs/2310.11667
Publikováno v:
ACL 2023
We present PESCO, a novel contrastive learning framework that substantially improves the performance of zero-shot text classification. We formulate text classification as a neural text matching problem where each document is treated as a query, and t
Externí odkaz:
http://arxiv.org/abs/2305.14963
The remarkable performance of large language models (LLMs) in zero-shot language understanding has garnered significant attention. However, employing LLMs for large-scale inference or domain-specific fine-tuning requires immense computational resourc
Externí odkaz:
http://arxiv.org/abs/2304.11872
Extreme Multi-label Text Classification (XMTC) has been a tough challenge in machine learning research and applications due to the sheer sizes of the label spaces and the severe data scarce problem associated with the long tail of rare labels in high
Externí odkaz:
http://arxiv.org/abs/2204.00958
Extreme multi-label text classification (XMTC) is the task of tagging each document with the relevant labels from a very large space of predefined categories. Recently, large pre-trained Transformer models have made significant performance improvemen
Externí odkaz:
http://arxiv.org/abs/2204.00933
Recently, a considerable literature has grown up around the theme of Graph Convolutional Network (GCN). How to effectively leverage the rich structural information in complex graphs, such as knowledge graphs with heterogeneous types of entities and r
Externí odkaz:
http://arxiv.org/abs/2006.07331
Publikováno v:
ICONIP 2020: Neural Information Processing
Change-point detection (CPD) aims to detect abrupt changes over time series data. Intuitively, effective CPD over multivariate time series should require explicit modeling of the dependencies across input variables. However, existing CPD methods eith
Externí odkaz:
http://arxiv.org/abs/2004.11934
Graph Convolutional Networks (GCNs) have received increasing attention in the machine learning community for effectively leveraging both the content features of nodes and the linkage patterns across graphs in various applications. As real-world graph
Externí odkaz:
http://arxiv.org/abs/1911.07123