Zobrazeno 1 - 10
of 419
pro vyhledávání: '"Sun, Aixin"'
In this paper, we propose the task of \textit{Ranked Video Moment Retrieval} (RVMR) to locate a ranked list of matching moments from a collection of videos, through queries in natural language. Although a few related tasks have been proposed and stud
Externí odkaz:
http://arxiv.org/abs/2407.06597
Autor:
Li, Xiang, Yao, Yiqun, Jiang, Xin, Fang, Xuezhi, Wang, Chao, Liu, Xinzhang, Wang, Zihan, Zhao, Yu, Wang, Xin, Huang, Yuyao, Song, Shuangyong, Li, Yongxiang, Zhang, Zheng, Zhao, Bo, Sun, Aixin, Wang, Yequan, He, Zhongjiang, Wang, Zhongyuan, Li, Xuelong, Huang, Tiejun
Large Language Models (LLMs) represent a significant stride toward Artificial General Intelligence. As scaling laws underscore the potential of increasing model sizes, the academic community has intensified its investigations into LLMs with capacitie
Externí odkaz:
http://arxiv.org/abs/2407.02783
Autor:
Ma, Yubo, Zang, Yuhang, Chen, Liangyu, Chen, Meiqi, Jiao, Yizhu, Li, Xinze, Lu, Xinyuan, Liu, Ziyu, Ma, Yan, Dong, Xiaoyi, Zhang, Pan, Pan, Liangming, Jiang, Yu-Gang, Wang, Jiaqi, Cao, Yixin, Sun, Aixin
Understanding documents with rich layouts and multi-modal components is a long-standing and practical task. Recent Large Vision-Language Models (LVLMs) have made remarkable strides in various tasks, particularly in single-page document understanding
Externí odkaz:
http://arxiv.org/abs/2407.01523
Autor:
Li, Xiang, Yao, Yiqun, Jiang, Xin, Fang, Xuezhi, Wang, Chao, Liu, Xinzhang, Wang, Zihan, Zhao, Yu, Wang, Xin, Huang, Yuyao, Song, Shuangyong, Li, Yongxiang, Zhang, Zheng, Zhao, Bo, Sun, Aixin, Wang, Yequan, He, Zhongjiang, Wang, Zhongyuan, Li, Xuelong, Huang, Tiejun
Large language models (LLMs) have showcased profound capabilities in language understanding and generation, facilitating a wide array of applications. However, there is a notable paucity of detailed, open-sourced methodologies on efficiently scaling
Externí odkaz:
http://arxiv.org/abs/2404.16645
Autor:
Sun, Aixin
Publikováno v:
SIGWEB Newsl. 2024, Spring, Article 4 (Spring 2024), 11 pages
Recommender Systems (RecSys) have become indispensable in numerous applications, profoundly influencing our everyday experiences. Despite their practical significance, academic research in RecSys often abstracts the formulation of research tasks from
Externí odkaz:
http://arxiv.org/abs/2404.13375
Autor:
Fan, Siqi, Jiang, Xin, Li, Xiang, Meng, Xuying, Han, Peng, Shang, Shuo, Sun, Aixin, Wang, Yequan, Wang, Zhongyuan
Due to the large number of parameters, the inference phase of Large Language Models (LLMs) is resource-intensive. However, not all requests posed to LLMs are equally difficult to handle. Through analysis, we show that for some tasks, LLMs can achieve
Externí odkaz:
http://arxiv.org/abs/2403.02181
Recommender systems have been widely used for various scenarios, such as e-commerce, news, and music, providing online contents to help and enrich users' daily life. Different scenarios hold distinct and unique characteristics, calling for domain-spe
Externí odkaz:
http://arxiv.org/abs/2402.14440
Autor:
Ma, Yubo, Gou, Zhibin, Hao, Junheng, Xu, Ruochen, Wang, Shuohang, Pan, Liangming, Yang, Yujiu, Cao, Yixin, Sun, Aixin, Awadalla, Hany, Chen, Weizhu
Scientific reasoning poses an excessive challenge for even the most advanced Large Language Models (LLMs). To make this task more practical and solvable for LLMs, we introduce a new task setting named tool-augmented scientific reasoning. This setting
Externí odkaz:
http://arxiv.org/abs/2402.11451
Conversational recommender systems (CRS) aim to recommend relevant items to users by eliciting user preference through natural language conversation. Prior work often utilizes external knowledge graphs for items' semantic information, a language mode
Externí odkaz:
http://arxiv.org/abs/2401.14194
Large language models (LLMs) excel in abstractive summarization tasks, delivering fluent and pertinent summaries. Recent advancements have extended their capabilities to handle long-input contexts, exceeding 100k tokens. However, in question answerin
Externí odkaz:
http://arxiv.org/abs/2310.10570