Zobrazeno 1 - 10
of 143
pro vyhledávání: '"Shao, Yingxia"'
Autor:
Li, Chaofan, Qin, MingHao, Xiao, Shitao, Chen, Jianlyu, Luo, Kun, Shao, Yingxia, Lian, Defu, Liu, Zheng
Large language models (LLMs) with decoder-only architectures demonstrate remarkable in-context learning (ICL) capabilities. This feature enables them to effectively handle both familiar and novel tasks by utilizing examples provided within their inpu
Externí odkaz:
http://arxiv.org/abs/2409.15700
Although instruction tuning is widely used to adjust behavior in Large Language Models (LLMs), extensive empirical evidence and research indicates that it is primarily a process where the model fits to specific task formats, rather than acquiring new
Externí odkaz:
http://arxiv.org/abs/2408.10841
Time series data mining is immensely important in extensive applications, such as traffic, medical, and e-commerce. In this paper, we focus on medical temporal variation modeling, \emph{i.e.,} cuffless blood pressure (BP) monitoring which has great v
Externí odkaz:
http://arxiv.org/abs/2408.08488
Recent advancements in large language models (LLMs) have significantly contributed to the progress of the Text-to-SQL task. A common requirement in many of these works is the post-correction of SQL queries. However, the majority of this process entai
Externí odkaz:
http://arxiv.org/abs/2408.04919
Graph Neural Networks (GNNs) have superior capability in learning graph data. Full-graph GNN training generally has high accuracy, however, it suffers from large peak memory usage and encounters the Out-of-Memory problem when handling large graphs. T
Externí odkaz:
http://arxiv.org/abs/2406.04938
Autor:
Lian, Jinqing, Liu, Xinyi, Shao, Yingxia, Dong, Yang, Wang, Ming, Wei, Zhang, Wan, Tianqi, Dong, Ming, Yan, Hailin
The Natural Language to SQL (NL2SQL) technology provides non-expert users who are unfamiliar with databases the opportunity to use SQL for data analysis.Converting Natural Language to Business Intelligence (NL2BI) is a popular practical scenario for
Externí odkaz:
http://arxiv.org/abs/2405.00527
Large Language Models (LLMs) have excelled in various tasks but perform better in high-resource scenarios, which presents challenges in low-resource scenarios. Data scarcity and the inherent difficulty of adapting LLMs to specific tasks compound the
Externí odkaz:
http://arxiv.org/abs/2404.00914
Personality detection aims to detect one's personality traits underlying in social media posts. One challenge of this task is the scarcity of ground-truth personality traits which are collected from self-report questionnaires. Most existing methods l
Externí odkaz:
http://arxiv.org/abs/2403.07581
Dense retrieval needs to learn discriminative text embeddings to represent the semantic relationship between query and document. It may benefit from the using of large language models (LLMs), given LLMs' strong capability on semantic understanding. H
Externí odkaz:
http://arxiv.org/abs/2312.15503
Learnable embedding vector is one of the most important applications in machine learning, and is widely used in various database-related domains. However, the high dimensionality of sparse data in recommendation tasks and the huge volume of corpus in
Externí odkaz:
http://arxiv.org/abs/2311.15578