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pro vyhledávání: '"Li, Cuiping"'
Large-scale knowledge bases (KBs) like Freebase and Wikidata house millions of structured knowledge. Knowledge Base Question Answering (KBQA) provides a user-friendly way to access these valuable KBs via asking natural language questions. In order to
Externí odkaz:
http://arxiv.org/abs/2406.14763
Gradient leakage has been identified as a potential source of privacy breaches in modern image processing systems, where the adversary can completely reconstruct the training images from leaked gradients. However, existing methods are restricted to r
Externí odkaz:
http://arxiv.org/abs/2406.09484
Autor:
Huang, Xinmei, Li, Haoyang, Zhang, Jing, Zhao, Xinxin, Yao, Zhiming, Li, Yiyan, Yu, Zhuohao, Zhang, Tieying, Chen, Hong, Li, Cuiping
Database knob tuning is a critical challenge in the database community, aiming to optimize knob values to enhance database performance for specific workloads. DBMS often feature hundreds of tunable knobs, posing a significant challenge for DBAs to re
Externí odkaz:
http://arxiv.org/abs/2404.11581
Knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB. Existing methods have significantly boosted the performance of KBQG via pre-trained language models (PLMs) thanks to
Externí odkaz:
http://arxiv.org/abs/2404.01923
This paper introduces LLM-Streamline, a novel layer pruning approach for large language models. It is based on the observation that different layers have varying impacts on hidden states, enabling the identification of less important layers. LLMStrea
Externí odkaz:
http://arxiv.org/abs/2403.19135
Autor:
Wang, Yanling, Zhang, Jing, Zhang, Lingxi, Liu, Lixin, Dong, Yuxiao, Li, Cuiping, Chen, Hong, Yin, Hongzhi
Open-world semi-supervised learning (Open-world SSL) for node classification, that classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but under-explored problem in the graph community. As only seen classes have hum
Externí odkaz:
http://arxiv.org/abs/2403.11483
In this survey, we present a detailed examination of the advancements in Neural Question Generation (NQG), a field leveraging neural network techniques to generate relevant questions from diverse inputs like knowledge bases, texts, and images. The su
Externí odkaz:
http://arxiv.org/abs/2402.18267
Autor:
Li, Haoyang, Zhang, Jing, Liu, Hanbing, Fan, Ju, Zhang, Xiaokang, Zhu, Jun, Wei, Renjie, Pan, Hongyan, Li, Cuiping, Chen, Hong
Language models have shown promising performance on the task of translating natural language questions into SQL queries (Text-to-SQL). However, most of the state-of-the-art (SOTA) approaches rely on powerful yet closed-source large language models (L
Externí odkaz:
http://arxiv.org/abs/2402.16347
Various works have utilized deep reinforcement learning (DRL) to address the query optimization problem in database system. They either learn to construct plans from scratch in a bottom-up manner or guide the plan generation behavior of traditional o
Externí odkaz:
http://arxiv.org/abs/2312.06357
Previous methods on knowledge base question generation (KBQG) primarily focus on enhancing the quality of a single generated question. Recognizing the remarkable paraphrasing ability of humans, we contend that diverse texts should convey the same sem
Externí odkaz:
http://arxiv.org/abs/2309.14362