Zobrazeno 1 - 10
of 91
pro vyhledávání: '"Wen, Zhihao"'
Autor:
Chen, Yuyan, Wen, Zhihao, Fan, Ge, Chen, Zhengyu, Wu, Wei, Liu, Dayiheng, Li, Zhixu, Liu, Bang, Xiao, Yanghua
Prompt engineering, as an efficient and effective way to leverage Large Language Models (LLM), has drawn a lot of attention from the research community. The existing research primarily emphasizes the importance of adapting prompts to specific tasks,
Externí odkaz:
http://arxiv.org/abs/2407.04118
Autor:
Chen, Yuyan, Fu, Qiang, Yuan, Yichen, Wen, Zhihao, Fan, Ge, Liu, Dayiheng, Zhang, Dongmei, Li, Zhixu, Xiao, Yanghua
Large Language Models (LLMs) have gained widespread adoption in various natural language processing tasks, including question answering and dialogue systems. However, a major drawback of LLMs is the issue of hallucination, where they generate unfaith
Externí odkaz:
http://arxiv.org/abs/2407.04121
Predicting Remaining Useful Life (RUL) plays a crucial role in the prognostics and health management of industrial systems that involve a variety of interrelated sensors. Given a constant stream of time series sensory data from such systems, deep lea
Externí odkaz:
http://arxiv.org/abs/2405.04336
Fine-tuning all parameters of large language models (LLMs) necessitates substantial computational power and extended time. Latest advancements in parameter-efficient fine-tuning (PEFT) techniques, such as Adapter tuning and LoRA, allow for adjustment
Externí odkaz:
http://arxiv.org/abs/2402.11896
Autor:
Yu, Xingtong, Fang, Yuan, Liu, Zemin, Wu, Yuxia, Wen, Zhihao, Bo, Jianyuan, Zhang, Xinming, Hoi, Steven C. H.
Graph representation learning, a critical step in graph-centric tasks, has seen significant advancements. Earlier techniques often operate in an end-to-end setting, which heavily rely on the availability of ample labeled data. This constraint has spu
Externí odkaz:
http://arxiv.org/abs/2402.01440
Voucher abuse detection is an important anomaly detection problem in E-commerce. While many GNN-based solutions have emerged, the supervised paradigm depends on a large quantity of labeled data. A popular alternative is to adopt self-supervised pre-t
Externí odkaz:
http://arxiv.org/abs/2308.10028
Autor:
Wen, Zhihao, Fang, Yuan
Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions. However, low-resource text classification
Externí odkaz:
http://arxiv.org/abs/2307.10230
Autor:
Wen, Zhihao, Fang, Yuan
Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions. However, low-resource text classification
Externí odkaz:
http://arxiv.org/abs/2305.03324
Autor:
Wen, Zhihao, Fang, Yuan
Temporal graph representation learning has drawn significant attention for the prevalence of temporal graphs in the real world. However, most existing works resort to taking discrete snapshots of the temporal graph, or are not inductive to deal with
Externí odkaz:
http://arxiv.org/abs/2203.14303
Semi-supervised node classification on graphs is an important research problem, with many real-world applications in information retrieval such as content classification on a social network and query intent classification on an e-commerce query graph
Externí odkaz:
http://arxiv.org/abs/2105.06725