Zobrazeno 1 - 10
of 98
pro vyhledávání: '"Zhu, Qiaoming"'
The availability of high-quality data is one of the most important factors in improving the reasoning capability of LLMs. Existing works have demonstrated the effectiveness of creating more instruction data from seed questions or knowledge bases. Rec
Externí odkaz:
http://arxiv.org/abs/2410.18693
Long-context models(LCMs) have shown great potential in processing long input sequences(even more than 100M tokens) conveniently and effectively. With significant progress, recent research has pointed out that LCMs can accurately locate token-level s
Externí odkaz:
http://arxiv.org/abs/2410.18533
Autor:
Qiao, Dan, Su, Yi, Wang, Pinzheng, Ye, Jing, Xie, Wenjing, Zhou, Yuechi, Ding, Yuyang, Tang, Zecheng, Wang, Jikai, Ji, Yixin, Wang, Yue, Guo, Pei, Sun, Zechen, Zhang, Zikang, Li, Juntao, Chao, Pingfu, Chen, Wenliang, Fu, Guohong, Zhou, Guodong, Zhu, Qiaoming, Zhang, Min
Large Language Models (LLMs) have played an important role in many fields due to their powerful capabilities.However, their massive number of parameters leads to high deployment requirements and incurs significant inference costs, which impedes their
Externí odkaz:
http://arxiv.org/abs/2405.05957
Event coreference resolution (ECR) aims to group event mentions referring to the same real-world event into clusters. Most previous studies adopt the "encoding first, then scoring" framework, making the coreference judgment rely on event encoding. Fu
Externí odkaz:
http://arxiv.org/abs/2310.14512
Autor:
Li, Juntao, Tang, Zecheng, Ding, Yuyang, Wang, Pinzheng, Guo, Pei, You, Wangjie, Qiao, Dan, Chen, Wenliang, Fu, Guohong, Zhu, Qiaoming, Zhou, Guodong, Zhang, Min
Large language models (LLMs) with billions of parameters have demonstrated outstanding performance on various natural language processing tasks. This report presents OpenBA, an open-sourced 15B bilingual asymmetric seq2seq model, to contribute an LLM
Externí odkaz:
http://arxiv.org/abs/2309.10706
Topic segmentation and outline generation strive to divide a document into coherent topic sections and generate corresponding subheadings, unveiling the discourse topic structure of a document. Compared with sentence-level topic structure, the paragr
Externí odkaz:
http://arxiv.org/abs/2305.14790
The goal of dialogue topic shift detection is to identify whether the current topic in a conversation has changed or needs to change. Previous work focused on detecting topic shifts using pre-trained models to encode the utterance, failing to delve i
Externí odkaz:
http://arxiv.org/abs/2305.14006
Discourse parsing, the task of analyzing the internal rhetorical structure of texts, is a challenging problem in natural language processing. Despite the recent advances in neural models, the lack of large-scale, high-quality corpora for training rem
Externí odkaz:
http://arxiv.org/abs/2305.13755
Autor:
Li, Feng Wang Peifeng, Zhu, Qiaoming
Extracting spatial relations from texts is a fundamental task for natural language understanding and previous studies only regard it as a classification task, ignoring those spatial relations with null roles due to their poor information. To address
Externí odkaz:
http://arxiv.org/abs/2208.06961
Publikováno v:
In Engineering Applications of Artificial Intelligence May 2024 131
